{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "homework1\n",
    "\n",
    "id自增字段\n",
    "api对应的url\n",
    "count单位时间内被访问的次数\n",
    "res time sum响应时间总和（毫秒）\n",
    "res time min最小响应时间\n",
    "res time max最大响应时间\n",
    "res_time_avg平均值\n",
    "interval采样间隔时间（秒）\n",
    "create_at创建日志时"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>2019162542\\t/front-api/bill/create\\t8\\t1057.31...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644\\t/front-api/bill/create\\t5\\t749.12\\t103...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742\\t/front-api/bill/create\\t5\\t845.84\\t136...</td>\n",
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       "      <th>3</th>\n",
       "      <td>162808\\t/front-api/bill/create\\t9\\t1305.52\\t90...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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      "text/plain": [
       "                                                   0\n",
       "0  2019162542\\t/front-api/bill/create\\t8\\t1057.31...\n",
       "1  162644\\t/front-api/bill/create\\t5\\t749.12\\t103...\n",
       "2  162742\\t/front-api/bill/create\\t5\\t845.84\\t136...\n",
       "3  162808\\t/front-api/bill/create\\t9\\t1305.52\\t90...\n",
       "4  162943\\t/front-api/bill/create\\t3\\t568.89\\t138..."
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('./log.txt',header=None)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2019162542</td>\n",
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       "      <td>/front-api/bill/create</td>\n",
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       "      <td>60</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "            0                       1  2        3       4       5      6   7  \\\n",
       "0  2019162542  /front-api/bill/create  8  1057.31   88.75  177.72  132.0  60   \n",
       "1      162644  /front-api/bill/create  5   749.12  103.79  240.38  149.0  60   \n",
       "2      162742  /front-api/bill/create  5   845.84  136.31  225.73  169.0  60   \n",
       "3      162808  /front-api/bill/create  9  1305.52   90.12  196.61  145.0  60   \n",
       "4      162943  /front-api/bill/create  3   568.89  138.45  232.02  189.0  60   \n",
       "\n",
       "                     8  \n",
       "0  2018-11-01 00:00:07  \n",
       "1  2018-11-01 00:01:07  \n",
       "2  2018-11-01 00:02:07  \n",
       "3  2018-11-01 00:03:07  \n",
       "4  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('./log.txt',header=None,sep='\\t')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                int64\n",
       "api              object\n",
       "count             int64\n",
       "tes_time_sum    float64\n",
       "res_time_min    float64\n",
       "res_time_max    float64\n",
       "res_time_avg    float64\n",
       "interval          int64\n",
       "created_at       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns=['id','api','count','tes_time_sum','res_time_min','res_time_max','res_time_avg','interval','created_at']\n",
    "df.head(2)\n",
    "#随机采样，多次执行，数据不一样，看大概\n",
    "df.sample(5)\n",
    "#多少条，多少列\n",
    "df.shape\n",
    "df.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            179496 non-null  int64  \n",
      " 1   api           179496 non-null  object \n",
      " 2   count         179496 non-null  int64  \n",
      " 3   tes_time_sum  179496 non-null  float64\n",
      " 4   res_time_min  179496 non-null  float64\n",
      " 5   res_time_max  179496 non-null  float64\n",
      " 6   res_time_avg  179496 non-null  float64\n",
      " 7   interval      179496 non-null  int64  \n",
      " 8   created_at    179496 non-null  object \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 11.0+ MB\n"
     ]
    },
    {
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       "      <th>3</th>\n",
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       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
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       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
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      ],
      "text/plain": [
       "           id  count  tes_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0  2019162542      8       1057.31         88.75        177.72         132.0   \n",
       "1      162644      5        749.12        103.79        240.38         149.0   \n",
       "2      162742      5        845.84        136.31        225.73         169.0   \n",
       "3      162808      9       1305.52         90.12        196.61         145.0   \n",
       "4      162943      3        568.89        138.45        232.02         189.0   \n",
       "\n",
       "   interval           created_at  \n",
       "0        60  2018-11-01 00:00:07  \n",
       "1        60  2018-11-01 00:01:07  \n",
       "2        60  2018-11-01 00:02:07  \n",
       "3        60  2018-11-01 00:03:07  \n",
       "4        60  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看内存占用空间\n",
    "df.info()\n",
    "#查看api列是否重复（freq）\n",
    "df['api'].describe()\n",
    "df=df.drop('api',axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  179496\n",
       "unique                 179496\n",
       "top       2019-02-13 00:30:08\n",
       "freq                        1\n",
       "Name: created_at, dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['created_at'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>2019162542</td>\n",
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       "      <th>2018-11-01 00:03:07</th>\n",
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       "      <th>2018-11-01 00:04:07</th>\n",
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       "    <tr>\n",
       "      <th>2019-05-30 23:06:21</th>\n",
       "      <td>13438800</td>\n",
       "      <td>11</td>\n",
       "      <td>2783.48</td>\n",
       "      <td>99.24</td>\n",
       "      <td>489.90</td>\n",
       "      <td>253.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:06:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:07:21</th>\n",
       "      <td>13438866</td>\n",
       "      <td>10</td>\n",
       "      <td>1951.10</td>\n",
       "      <td>85.37</td>\n",
       "      <td>529.51</td>\n",
       "      <td>195.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:07:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:08:21</th>\n",
       "      <td>13438917</td>\n",
       "      <td>3</td>\n",
       "      <td>494.17</td>\n",
       "      <td>103.95</td>\n",
       "      <td>211.47</td>\n",
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       "      <td>2019-05-30 23:08:21</td>\n",
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       "    <tr>\n",
       "      <th>2019-05-30 23:09:21</th>\n",
       "      <td>13438981</td>\n",
       "      <td>9</td>\n",
       "      <td>1798.28</td>\n",
       "      <td>101.11</td>\n",
       "      <td>433.30</td>\n",
       "      <td>199.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:09:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:10:21</th>\n",
       "      <td>13439086</td>\n",
       "      <td>6</td>\n",
       "      <td>1017.97</td>\n",
       "      <td>74.45</td>\n",
       "      <td>298.97</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:10:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179496 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id  count  tes_time_sum  res_time_min  \\\n",
       "created_at                                                           \n",
       "2018-11-01 00:00:07  2019162542      8       1057.31         88.75   \n",
       "2018-11-01 00:01:07      162644      5        749.12        103.79   \n",
       "2018-11-01 00:02:07      162742      5        845.84        136.31   \n",
       "2018-11-01 00:03:07      162808      9       1305.52         90.12   \n",
       "2018-11-01 00:04:07      162943      3        568.89        138.45   \n",
       "...                         ...    ...           ...           ...   \n",
       "2019-05-30 23:06:21    13438800     11       2783.48         99.24   \n",
       "2019-05-30 23:07:21    13438866     10       1951.10         85.37   \n",
       "2019-05-30 23:08:21    13438917      3        494.17        103.95   \n",
       "2019-05-30 23:09:21    13438981      9       1798.28        101.11   \n",
       "2019-05-30 23:10:21    13439086      6       1017.97         74.45   \n",
       "\n",
       "                     res_time_max  res_time_avg  interval           created_at  \n",
       "created_at                                                                      \n",
       "2018-11-01 00:00:07        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "2018-11-01 00:01:07        240.38         149.0        60  2018-11-01 00:01:07  \n",
       "2018-11-01 00:02:07        225.73         169.0        60  2018-11-01 00:02:07  \n",
       "2018-11-01 00:03:07        196.61         145.0        60  2018-11-01 00:03:07  \n",
       "2018-11-01 00:04:07        232.02         189.0        60  2018-11-01 00:04:07  \n",
       "...                           ...           ...       ...                  ...  \n",
       "2019-05-30 23:06:21        489.90         253.0        60  2019-05-30 23:06:21  \n",
       "2019-05-30 23:07:21        529.51         195.0        60  2019-05-30 23:07:21  \n",
       "2019-05-30 23:08:21        211.47         164.0        60  2019-05-30 23:08:21  \n",
       "2019-05-30 23:09:21        433.30         199.0        60  2019-05-30 23:09:21  \n",
       "2019-05-30 23:10:21        298.97         169.0        60  2019-05-30 23:10:21  \n",
       "\n",
       "[179496 rows x 8 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#当前索引\n",
    "df.index\n",
    "#用created——at作为目录索引\n",
    "df.index=df['created_at']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2018-11-01 00:00:07', '2018-11-01 00:01:07', '2018-11-01 00:02:07',\n",
       "       '2018-11-01 00:03:07', '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "       '2018-11-01 00:06:07', '2018-11-01 00:07:07', '2018-11-01 00:08:07',\n",
       "       '2018-11-01 00:09:07',\n",
       "       ...\n",
       "       '2019-05-30 23:01:21', '2019-05-30 23:02:21', '2019-05-30 23:03:21',\n",
       "       '2019-05-30 23:04:21', '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "       '2019-05-30 23:07:21', '2019-05-30 23:08:21', '2019-05-30 23:09:21',\n",
       "       '2019-05-30 23:10:21'],\n",
       "      dtype='object', name='created_at', length=179496)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-11-01 00:00:07', '2018-11-01 00:01:07',\n",
       "               '2018-11-01 00:02:07', '2018-11-01 00:03:07',\n",
       "               '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "               '2018-11-01 00:06:07', '2018-11-01 00:07:07',\n",
       "               '2018-11-01 00:08:07', '2018-11-01 00:09:07',\n",
       "               ...\n",
       "               '2019-05-30 23:01:21', '2019-05-30 23:02:21',\n",
       "               '2019-05-30 23:03:21', '2019-05-30 23:04:21',\n",
       "               '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "               '2019-05-30 23:07:21', '2019-05-30 23:08:21',\n",
       "               '2019-05-30 23:09:21', '2019-05-30 23:10:21'],\n",
       "              dtype='datetime64[ns]', name='created_at', length=179496, freq=None)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index=pd.to_datetime(df.created_at)\n",
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.0\n",
       "mean         60.0\n",
       "std           0.0\n",
       "min          60.0\n",
       "25%          60.0\n",
       "50%          60.0\n",
       "75%          60.0\n",
       "max          60.0\n",
       "Name: interval, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.interval.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([60], dtype=int64)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.interval.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['id','interval'],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>tes_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177832</td>\n",
       "      <td>108.419626</td>\n",
       "      <td>359.880374</td>\n",
       "      <td>187.812208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.486073</td>\n",
       "      <td>79.640693</td>\n",
       "      <td>638.919827</td>\n",
       "      <td>224.464813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count   tes_time_sum   res_time_min   res_time_max  \\\n",
       "count  179496.000000  179496.000000  179496.000000  179496.000000   \n",
       "mean        7.175909    1393.177832     108.419626     359.880374   \n",
       "std         4.325160    1499.486073      79.640693     638.919827   \n",
       "min         1.000000      36.550000       3.210000      36.550000   \n",
       "25%         4.000000     607.707500      83.410000     198.280000   \n",
       "50%         7.000000    1154.905000      97.120000     256.090000   \n",
       "75%        10.000000    1834.117500     116.990000     374.410000   \n",
       "max        31.000000  142650.550000   18896.640000  142468.270000   \n",
       "\n",
       "        res_time_avg  \n",
       "count  179496.000000  \n",
       "mean      187.812208  \n",
       "std       224.464813  \n",
       "min        36.000000  \n",
       "25%       144.000000  \n",
       "50%       167.000000  \n",
       "75%       202.000000  \n",
       "max     71325.000000  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#初步分析，直方图\n",
    "#表示接口调用分布状况，大部分在8-9次内\n",
    "df['count'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf615f10>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eNyv5/cIjd+Uvr3zECz/6LLsP7MnDby/iskfeabdd44at7ZZ1pvG0cUNwgrt4zWZOumki53x6KFefum9y+ZQFqzj91kkpZeePPTmwerNhnrxhdJJChxNICNXUj4PJsU5obuP6NsHKJvBAisCD//bSNk++cFd+4apUb3zqQifnPnFu0m0sB1a7HvrbC1L7D6R3aisWJvKGEQLxjfB6SMbkO72L9stjcAKjcggm8oYRAuUoUoWanO7IR2ScNMMlEJEXkbtEZIWIzPQs6yciz4vIXPe9bxB1GUZZkIdiBn0jSO6uQJH1G2PPliYahMbH4UYRlUMIypO/GzgxbdkY4EVVHQ686H43jIognzz5IPO8oW1Mm0LFxe89p5gplEbhBCLyqvoKsCpt8SnAPe7ne4BTg6jLMKKK1xHOxykOerTGTj8Z+Nw+KfKm8ZEmzJj8IFVdCuC+DwyxLsOIFPnoZSKPunHDNu7974Lk8pvHf8DYZ2YzeX6639QxN704F4A35vnbLsGtEz5MGS45nSkL2rKALvrn22xpbgFgwpz2I2VmYvGazfzrzY/ztuf+Nz9m2drSZKR0hoemLALgPTdX/rUPGtn/V8/x3LvLS2JPJPLkReQC4AKAoUOHltgawygOiV6WH6/axBWPzeTI4dvTp3sXfv+fOQDcNuHDotrz5rxVnHjjxKzrT7+1bcz0cTOWJj8/MHkh156xX879n3PnG07fgP0Gs11dLZD96WPVxm0Z8+OjyPJ1qWmed78+P/n5b6/N47fPzAZg3DtLKQVhevLLRWQwgPu+IltBVb1dVRtUtWHAgAEhmmQY4eENW+TTiJk+LnxTi9Ia4+71n7i57/kcYzkNM9BRtGrjtpai2ZGNMEX+CeA89/N5wOMh1mUYJSclJu+zfJzIdrOrcmf5iNtxV6XPXhIxgkqhvB+YBOwhIotE5JvAWOA4EZkLHOd+N4yKIB8hy1Qk6FmfSoE328brkFe56p/PMUZlvtp8iLqpgcTkVfUrWVYdG8T+DaPcyCeFMpOQxW3yilZVql3Rl+Sy0tkTBlG/MVuPV8MIg4I6Q2nOSbrLDa8AJsa48d4As3aoKqO8TBN5w6hA8vnbZxKH5phNXqEp4RrnPW6TMEX9ySRWIn/x/VN55O1FpTbDqGA+XLmB+jHjuHPiR8llLa1K/ZhxHD72pZSy6Rp/y/gPI+8V+mXPK56lfsw4Fq3e5Csmf/H9U8M2LTDSf1cvf3T7LmTjP+8u4xt3vxW0SSnESuSfmL6EHz04vdRmGBWKAjc8/z4Ad0ycl1y+ZpMz9OziNZvblffyyNTFZZU6mI1MkZZ7Xp+f9OSjeoSnHTCk6HVeeO8UXpqdNbs8EGIl8oZRajI5qdkaUzN5tHHNk6+trkrG2aN6jKcftFOpTQgFE3nDCAghS5w9m6hlWBwHTz4Tjsg7n73nKErRqVI29YaZMmoibxgBkum/mm0O10w3hDikUGYSyy41VcmYfFSPsZQZPWHe7EzkDSMglMzCnWlC50T5dKIqgJ2ltlo8nnxpbclGKTuuhtngbiJvGJ0kpRt/hvVbs4l8pvh9lOIXAVIl4iu7phSUcniCMM9IbER+3ZamnGVUtay6Sxulw891kjqOfAZPPi1ck2h4zCTo5Z4nn+28qbaFcZpblJZW578YJa++lJ58mE9wkRhqOAhufL7jfFSAU29+jemL1jJ/7MlFsMgoV56YvoSL75/KhJ8exbD+PXxtm0njTrulbYjeiXNX8rW/vslTPziCKx6b2a7sl/8yybe9UWKXy57OuPyap2clP4/+Y/bhjEtJ7261Jav7vLve5IELDw1l37Hx5PO5C09ftDZ8Q4yy56npSwCYtTT7BBpevOGaXKGIRE50oRN7GMHwp68ckPx8/Zf3545zG6j3eUMPkjCvh9h48lEf7tMoHzqTZJHrqbs6mWESs779RWRY/+4s+GRTp/axY5+65OfTDnTy46Oav99ZYuTJm8gbpUU199iT1dUJkQ/fnrhSHch/vf0+4iohsRH5uP5ARukopI0+V4OtefKdJ4j/eqZ9lNPIl36Ij8iX2gAjNkiBV5OI5Lwx1Lhhxbj2bC0GQYhxJelFfES+kn41I7LkanhNtB1l6wVr5CaQYI0rGJXQlBcbkTeMoMnH1561dB3rtzQD8NHKDTk9+RluhtdLs1d20rrKJZBwjfteHSGV/2DFhlD2GxuRL/QR2zDS8SMiJ900kYemOHMY3PfGx3mnUOabnmm0J4j/euI3jlIc/nPXTwhlv/ER+ej8VkYFk0njDxzaJ5S6ulQ7f9+T9tkhlP1HFe9//TPDty9sH+6NIphMnWgTG5E3jKDJFXrJlFedKYkyrDbWrrXO37d7l9h0d/FNbXXnJCxK4ZqwiI3Ix/+nMopFvs5dpgyZTIIe1nhJbXHlUHYfWbwhlkL/94ldVIDGx0fkLV5jFJt8x4MPa2TJhNhVgjfqxXu0nf3bV0JP+fiIvGEETK7+q5k8+YwiH1K2ZELgKk7kA+wMZTF5w6hA8s3eyCToxZy3NRmuiYhQFWsY79TDLbDjmrtdlLJrwiI2LTZ+fqp/vvExB+/Sj90H9gzNHiN+TP14NTOXrGPd5iamfryGfYf0blcmk/DPWb4+FHuSHXoi4MmfdNPEQNJC316wOmcZ7024UI1uewoqbPtyIjYi74fLH32HrjVVzLn6pFKbYkSYdMf0S55x4QFemLW83TbFnPUoKE9+u7oahvTtzjF7DuDm8R8WtI+g8v79DveQ68j33KEXs5e1v8nmG64Z0qcbi9dsTll20j478MzMZX7MLCmxuY/5vc6zTclmGJ1J1Qp7jlbvhDeFxuQ/Vd835fuMK0/gmUs+w2dHDExZ3q9Hl8KMDBnv4Xr/9yN33K5d2WcvPTLjPhJPA11qOpbAa0/fr92yvQZvx/yxJ5fN5EPxEXlLojQiQCnGHfMbrsn2X0nfTW11RP9TKSmUbZ/9nPtEo3oukY/DGEOxEXnDCJpC9DpoT76jJ9REZCiohtf03XS2o1ExSJlE3UeoLDGXbq5jTJ+ftxwJPSYvIvOB9UAL0KyqDeHUE8ZeDcMfQYt8lUjOPPugUijTM026RFTksx2tn+aQhHjnEvk4ePLFang9WlUbw6zANN4IisS1VEhKYNANr1XieEcdEZjIp32PqicvWWLyfs59k9sml+tGFgeRj+avaBgRoBDBDl7kswt4oibfIp+leLonX1sTTdcppcdrSkzeh8gnwjU5jrGpOcP4RGU230sxPHkFnhMRBf6iqreHUUlisCaA+jHjuPiY3QFYtm4Lvztjf7Y05fKHDCOV1lZYs2kbn//Tq+w9uH3mRiaCdvzyCUP6FfmaLOXTF/fv0dXXfotF7261yc/du1QnP/sR38R57dOt4wyiKPRB6CzFEPnDVXWJiAwEnheR2ar6ireAiFwAXAAwdOjQgir50gE78ZunZye///GlD5Kff3fG/sxdHs6A/Eb8SHi0rapMnNvIotWbWbR6c46tSG4TJN5G1ed/mDkd0CvaV5+6D798bGa7Mgfv0o+bzhrFk9OXsGTNFl7/8BMADt+9f7KM1yv+1RdHcsqoHfnHfxdw3XPvd/o4guKyk/bk3EPr2et/nwXgqlP2YdO2Fsa9sxQF7vvWIWxrbmVbSyt9uzsCft2Z+6OqLF+3JXksh+7an5+esAdfPSRVb24++0AG96ljw5ZmFDh8t/785N/Ts9rz1A+O4Jpxs5j00SehHG8QhB6uUdUl7vsK4FHg4AxlblfVBlVtGDBgQEH1DOjVsdcR1iBRRnwp5JJp9uHK3/LVA3OWSYRrutRUMXxQr4xlEp78EbtvzzmfHsYJIwe1K3PxMcMZ3LsbFxy5W8ryQ3f1iLyr8b3qajjvsHr6dO/C948ZntexFIsLP7sb3Tzee7cu1fz0hD0A5wZ7+O7bc/SeAzlh5A4cvEs/AM44aCfObNg55ViqqoSLjt6dPt1TPfmT9xvMgUP7cuSIAXx2xABqqqv4ysE7Z7VnnyG9ObNhp8COL4yhIUIVeRHpISK9Ep+B44H2bkYRCLuTihE/CnEM/PTYzCcS0FG4ICEINWmNh376jKQM21umkYkqz5NXGOTabZAN1GEcQtjhmkHAo+6FVAP8U1WfDbnOjBSzu7lR3iS0rpBrxk82RkeNqgnyibcnwjWFiHRVlo5F5UTiEFpDSoRJvw7SRycNUuRbVakK+HcIVeRV9SNg/zDryBfz5A2/FHLJJLI28iEfkc+vjPOe0KJcm3hDAt6yVQFoVW21+DoHQSDJ4y+NJ98lwCykMGSqYlIowxru1YgvYV8z+YhqQsAzyYgmy6Su9ePRewUsCE++a0117kIBkzj+sH6tXPutCeLumKyrzGLyUaFxw1bWbG5qt1xVWbelidZWpbmlNRYdH4zCaHJ/f1Vla7OTbtuqGmrqbVDhmuq0cE0msc5WlVdUgsgWzDUWTBiEHZPPtd9Kj8lHgoarX8i4/Lrn5nDz+A/5n4adeXP+KuY1biybkeWMYDni2pfY2tzKRUftzn/edYYQnrJgNU/NWOprPzVVknfja6Hhmh1717Fk7RYOGtqXF2evoK7W8Z4TArHHDr0Y906q3f17Zs4HT/Hkk3GPPIzPwn479eblOSsL30EH1NVmFtNEts2onfvktR9vnn0+5BLegdsF158gjBtVRYh8Nu5742MAHpi8sMSWGKVm+bqtADw+fXFy2RvzVvnez059uzH/k015la2truKNy49l8ZrN9OlWS3OrcvwNTheScw8dxt8nLcjoyY+7+DM0btjKkL7d+GjlRlZt3Jay/qKjd2fUzn1QnBvChq3N7LlDW2eubDKSGHUy/SY17uIjWLhqM9/5xxQAnvz+EXzhz68CcOe5DXzr75OTZS8fvVfgIn/VKSOp79+DvTMMJQyOaD/1gyPYdUCPnPt64UefpW93vyKf1vCadgJ3G9CTz+01kBdmreCrhwzl/MPqOe6GlK5A9Oxaw91f/xQKnHnbpKx1hREhrGiRby5yA5FRXhQSk/fT6NilpopB29UxaLu6duvOOGinrCLft0cX+rpjve8zpDevvJ8qqtVVwpEj8utv4hWwRKilOS1NZeSOvdnLc5PYd6e2GbHS+6fUhRCTP3BoX/bJMAuXl1zrExQyG1ziMqjv3z3rDXzfIX14YdYK+vXokrE/w0HD+tJQ3y9nXWWXJx910i9mozLZuLU543K/sxSBvxTKrh3Er9MzZcLKYfdqSmKwrkw3qmz1p8fgq0MYg77U+fuJs9HRfLCJMEtn54y17JqAMU/eAGd8o0wUknbr58bQUSNlcvCxkBXOa21H9mQTr/RtakMY66XU+fuaFPDcZbIdfr5XhXnyASJSmKdmxI9la4MT+SYf00p2NMxta1I0ghc4r46kePIFZMakH0N679sgKLknn3iq6qBM4lLJ9nvlK97myRtGCCwNUuR9hAA79OTz7NiUb5m87ClAoNOHXQhqbHsvJRd5Um+4ma6K1hyefN51mScfHDbKQWUzv3Ej8xo3AvDszLZ0ww9WtI1WWsjUb1uaghF5P3mMnbmWvXnyhcST04ctDmNe2FKHaxL37Y6eqlqTN+VsnnyedZknbxjBcNR1L3P0dS+jqrwwawXgeIx+RNoPnxm+ffJzYnjfnl2zJ7cl/uxd3Rz4rx++S2C2fG7vtlEqM4nKmQflHlWxV10NIwb1bJdzHmTvz4ZhfQHYIUP2EcAhu+TOVgmChJd+5AjnN/T+lgmO2sPJZjp89/brAE4ZtWNedYUx/EqsUigf/d5hfOmW10tthlFGeL31sJ7ubjvnILrWVDFxbiOjdu7Dvd84hC3NLclOTJlI2FJbJTk76Pl1wD87YgCXfm44N74wt926eb8dndUb9drxzpUnoKopZaurJKMnP+uqE5Pjv/vhvMPqeei7h+W0JWwSl8VBw/rxi5P3zljm07v2z3rustk677ejmbxgdUrefBi97mPlyYcRDzTizTYfDaWF0q1LdUoqZFWV0L1Lx/6V+mh4LeTmlAyBpG3sJ2STXjZd9BN4x3/3Q1QiqppsVO24nN9wV6byW0O4HmMl8mFkIhjxphgiD55GO7+x2ZAu6QBGMGhH0JGGsEaV9IsGlAOfifQ9hs0WQ14AABJXSURBVHE9xkrkTeMNvxTSuFoIfq/NRINoPpt15rqPiI5GmjDvt+m/XRjXY6xE3sI1Rj4kRpmE4nvyeQtyjrzrzpLYaxhD2wZFVG5AydBZKGqZ+vuaJ58DC9cY+bBhS9swBsUSeb+XZltKXvC2ePcbFSHNRFRuQMnfwte0ioXVZSKfgyAc+dsmfMicZetRVcY+M5tfPfmuTTgSMzZ4xqq59IFpRanTb0w+Ga4JTeTDnWgjCKJyA2obuyb4faePTbStJfj5C2Il8sP69yio156Xsc/M5vFpi1m9qYnbJnzI316bz6LVmwOy0IgCm7a1/ZHeXbIu7+3SO/4kuOv8hpTv3jzqUTv34VP1fZM+YC5H5MsNOzH2tH05aFhfDhjah1+Mzpyy5+WgYX0ZtXMfLh+9V86yCc5s2Il9hmzHuYcOy3ubbPz2tH0BuONc5zx864hd+OXJezHmpD258Mhds2633069Of3AnfjlyXvxhf2dPPIDhraNCX/sXoOybVpULh+9J6N27sPBPvLy//6NgwG4x31P39+3jnD6PSQuhx5dathtQI9QZtaSqLRgJ2hoaNDJkyfnLtgBb81flcw9PX7vQTz33nJf25976DDOPXQYn7veGRP60e8dxgFD+3bKJiM6TF+4hlNufi1l2Y+PG8Efnn+/w+3eufJ49r3yuXbL5489mb+9No9fPfkeAG/+4lgOvuZFtu/Zhcm/PA6A1z9s5Ow73uCQXfrxwIWHBnQk5UP9mHHtltkEPTBt4RpOvfk19t+pN49//4hO7UtEpqhqQ/ryWHnyCbyjSxbSg2zDlmY+2dA2EUP6pAxGeZMpg6FflpmTvHTUmzOzr9TmtieuySCnijPiQ5iudiyvOK+wtxTwpLJ+a3OKsH9iIh8rMjVu9e+RW+Q7yq7QDF+8MdzE3AU1IYztYpQvySynEFU+liLvHQmwYE9+o3nycSWTyPfrkXuezo49+bbrLFNedWIijiDHdjHKn7ZOaeGpfCyvuM6GazZua/Pkq6vERD5mZOo63qsu9zBOHTWaZhujPUHimuxSY5680UYxRtiM1QBlCVo8nrzfiUG2q6thwxZH5HvV1dCra01KfN4ofzLF5PPpSNdRt3avJ5Y+/ji0DTxlnryRiTDDNbEUeW/jVvpQqLk4eJf+vDh7OYve3MyOvevoVVfLE9MXM37OiqDNNErE1qb2ucjZ0iPzxTvgWGLKvv6extzE2PH98oj9x5G62qrQhnEuZ7rWOtdFn+7+dMoPsRT5o/cYyC9G78W2llbOPngo7x66juffW8ag3nUM7FVHU0srE+e2zXB/6qghPPz2IvbZsTfH7DWQHfvUoQqH7tafutoqxs9e2UFtRjmyetM2+vXowohBvVBVdh3Qkxv/ZxQvzl7B7KXrGL3vYFZv2sbazU08Pm0JFx87HIA7z21g1cZt9O5ey4crN3Dors7Y8Gd9ameenbmMi47enYHb1fHb0/bl6D0GJus7ceQOXD56T875dOfz0suRZy85kmufnc0PjhlOlxph1tL1pTYpEowY1ItfnzKS0fsODq2OWObJG4ZhVBoVlSdvGIZhOJjIG4ZhxJjQRV5EThSROSLygYiMCbs+wzAMo41QRV5EqoGbgZOAvYGviEjuEZcMwzCMQAjbkz8Y+EBVP1LVbcC/gFNCrtMwDMNwCVvkhwALPd8XucsMwzCMIhC2yGfqYdIuZ1NELhCRySIyeeVKy0k3DMMIirA7Qy0CdvZ83wlYkl5IVW8HbgcQkZUisqDA+rYHGgvcNkzMLn+YXf6Jqm1mlz86Y1fGnnahdoYSkRrgfeBYYDHwFnC2qr4bUn2TM3UGKDVmlz/MLv9E1Tazyx9h2BWqJ6+qzSLyfeA/QDVwV1gCbxiGYbQn9LFrVPVp4Omw6zEMwzDaE7cer7eX2oAsmF3+MLv8E1XbzC5/BG5X5AYoMwzDMIIjbp68kQXpaMYLo6yw39LwQ1mJvIgMjuIFLiI7ikjuSUKLjIjsKyI/B9AIPbKJyA6ltiETIjKo1DZkQ0T2EJGTIHK/5TARGVpqO9IRkbpS25CJUmhYWYi8iHQVkVuBCcDtInJaqW0CEJGeInI98Axwp4ic7S4v6XkVh+uAfwI1IhLetDM+EJFuInIj8KyI3CAikRjiwv0dbwCeEZG/ROX6gqRtfwDuByIzrZT7W96Ac+3fIyLfdZeX+trvISK3A/8nIv3dZSV3DEupYWUh8sAXgcGqOgJ4CrhKREaU0iAR2RG4G+ePdzjwOJDwmks9z9kAYDBwkKpeo6pNJbYnwUXAAFUdBTwG/EZEdi+lQSIyBLgX578wGudP+LtS2pRARLYDHgGOUNUDVfXxUtvk4WJgR1XdG7gSuBRKe+273vtVwBFAL+Bo16YoPPmUTMMiK/Ii0tPzVYGVAO6F/ixwoYj0KYFdvdyPa4Efq+r3VXUDMAh4TEQGuOWKem49dgH0Boar6jYROUFEfiIiJxTTHo9dPd33aqAvzgWOqk4ANuJ4XL1LYZvLFuBOVb1EVZcBDwLTRGS/EtqUYAvODehdABE5XESOF5Hh7vei/39FpNqtV4AZ7uIdgXEismex7XFt6u5+3ArcChwJzAUOEpHd3DJF9+ajomGRE3kR2V1EHgTuFpGTRaQHsBlY53rPAL8HDgRGutuE/gOm2wXUquoCEekuIpcAY4AeOBf73qraWmS7/uaer37ABuA1EbkK+BmOWNwoIuelXXjFsOseEfm8u3g9cIiI7O/eDGcDI4Bd3W2Kcb72EJHbRKQbgKp+ArzsKbKza8+csG3Jw7ZtwEuAisgy4DfAccAEERlZxGssaZeqtrje+hJgqIhMBK7F+W1fEJHjiiWoIjJcRP6OE/74ItBLVT9Q1UZgPFBHCbz5yGmYqkbmhXPTeQq4AmdI4luBsUBXYBzOuPRd3LJXAg+VyK6bgT+56wQY4Sl7FfB8iey6BbjOXfcnHPHa3/1+BvAQzh+h2HbdBlwD1Lrv/wamub/nr4Hbi3S+jgDeBFqBXyR+v7QyewCPFMOeXLZ5zuWxwE/SrrFnI2BXb5wnnx3cZRcBTxfJrq8B7wHfBb4B3AGcm1bm28ANOGHLYv2OkdOwol7IeZygIcA/gGrP9zeBQ4Azgb8BB7vr9nR/2NoS2TUJ+KL7XRJigeMFPgZ0K5Fdb+A8ru4PPA98w1N+PE4ctVR2He9+3wXo534+Hfhh4jyGbNdewD7A7sAHwLAMZc4Cfu9+/jawX9jnK4tt9Z51dWllh+PE6utKZZd7zQ9xRXRXd1lXHMeifxHsOh74guf7tcB33M817vtQ4JfA93CetI8sgl2R07BIhWtUdTHQgPNImvh+C/ArVf03zmBnl4nIj3EmIPlIi9ComMWuW4Efut9VVVVEDgXuAl5X1c0ltOsKVZ2O03vuCyJymftYPRNYVSK7bgYud7/PU9VVInIk8CPcOQfUvfJDtGsWziQ2H+DcAK+CdrHtY4H+IvIwcDZOqCt0Mtj2K9c2UdWkDSJyGPBX4L/e5cW2y/2tluHccL4tIufjjFH1Fk57Vdh2PQc8J84giOD8Tju665rd94+BnsDVODfvUl37pdWwsO9sWe527bxc2u585wOvepb3wXkk/BSO9/AZ4CbgnAjYdb9rTw+ci38q8OUI2PUAcJj7fSTwY+CsCNh1P643hePBz8UZlTR0uzzrEk9cvXA802PT1j+D09B5RtB2dcY2HLH6OU6Y638iZNd+OJ7yuGJdY1nK3QeclrbsU8BS4Ksh2NUP2M57jmh7giiZhmW0tRiVpJ2cscCTwAHu96q09dU4jU2XepbdA+wTZbuAUVG0K8Lnq08p7ErY5r5fCjzlfv6K+wc8qlTnLIdtNXjafiJkV2hhyTztqgK6A4/iZLgJcALQNUS7rgBm4XjiV6bbVqr/ZLZXsdP8vuX+AHOB06B9Xq2qtgA/BS4RkVNF5BycmGBo+bedtCuxflrE7Irq+VJ3/ZpS2OXS6q67EThcRNYCn8MRhpeDtisg22pV9f2I2XUMbt+7UtnlLuvtvk6mrU0qcLvE6Wj1e5zr+Cjgf4FLRaRePZlOpfhPdkjYdxHcBjb3c1+c2aGOBP4CjHaXi6dMlft+Ck4I5BWcziBml9nVabs8ZXvjpLHNAA4P2q4o2xZDu76AI6APAp8Jyy6cp6mjcMMy7rI78CQ3uMuKcu3nbX9oO3YuiDuB13FidiPT1l0C/BE3rkVb3C/sDAuzq4Lt8pSpIqTMmajaFmO7egAXhmzXRbjhMvc8CE5v9/GkhWrDvvb9vsIM11yGE5v6Js5dOTlOsqquxUlBFJz8bdQ9O4l3s8vsCsMuT5lWVZ1BOETVttjZJSJVqrpRVf9SBLv+5tbfiuPVN+H0sl3s3agI174vAhd5cUikNd2nqrNU9Rpgm4j8ylN0Js5dcF8R+amIfDfMXl9ml9kVdk/MqNoWZ7s0hLFysth1tdcuddIedwGaVXWliJwmImcFbUsQBC7y6tCMk7N6kGfV94DviUhft9wmnLvzWcAFOPmiod0BzS6zK2wPK6q2mV3h2IWTC99dnKEVfo7TbyB6aPBxrESjw4E4A/J086y7A/iZtsW7PsLTXTvMl9lldlWqbWZX4HZd5n4eAywHvl2sa6yQV8HT/4kzGUULcJN6emyJO0KdqraIyL+Azar6dXfdT4BlqvoP93sXdQZhCgyzy+wK064o22Z2FdcuETkAmK1F6N3eKQq4y9XhdE9fDEzGHQDLXedNodsFZyyLiW75s3BSr07zW6fZZXaV2q4o22Z2Fd2uUHpDh3bdFnCCBGeSjGqcoU+vxzOyIbAD8HfgvzijDo7CGSXuOeD00A7E7DK7QrQryraZXfGwK7TjzeOE1AA/AXb2LKtz3wfjtHofT1t+9PHAd0M33OwyuyrUNrMrHnYV65Xr5OwLvI3TuHB/2rrECbkUZy7RdkPY4o53EcKPZnaZXaHZFWXbzK542FXMV64UykacnmZ7AvUicjyQmMotwc1AN6BBRD4rIl9yy4g6YziEgdlldoVpV5RtM7viYVfxyONO2M19vxB42XsXpC3V6Ns44zl/AJxajLuT2WV2VaptZlc87CrWy9eJwpl89uK05fvjTMM1tiQHYHaZXRVqm9kVD7tCP26fJ+kE4A338z44nRS2owjTfZldZpfZZnbF1a4wX76GNVDV/wCrRWQrzpyKXVV1nToz3pcMs8vsCpuo2mZ2xcOuUPFxB6zCmStxARHqxmt2mV2VapvZFQ+7wn75GtZARE4CXlLVrb7uJCFjdvnD7PJPVG0zu/wRVbvCpOCxawzDMIzoU9Q5Xg3DMIziYiJvGIYRY0zkDcMwYoyJvGEYRowxkTcMw4gxJvKGYRgxxkTeMDIgIvUicnYB290tImcUsN35IrKj3+0MIxcm8kbsEZGaAjarB3yLfCc4HzCRNwLHRN6IBSJyrojMEJHpInKv61FfLyLjgWtFpIeI3CUib4nIVBE5xd2uXkQmisjb7uswd5djgc+IyDQR+aGIVIvI793tZ4jIhe72IiJ/FpH3RGQcMDCHnf/r7mOmiNzubn8G0ADc59bXLbwzZVQa1uPVKHtEZCTwCHC4qjaKSD+ceTu3B05R1RYR+Q3wnqr+Q0T6AG8CBwAKtKrqFhEZjjN7UIOIHAX8RFU/79ZxATBQVa8Wka7Aa8CZ7j6+C5wIDMIZsvZbqvpQFlv7qeoq9/O9wIOq+qSIvOzWNzmEU2RUMIU8xhpG1DgGeEhVGwFUdZWIAPxb22b2OR74ooj8xP1eBwwFlgB/FpFRQAswIksdxwP7eeLtvYHhwJE4N4YWYImIvJTD1qNF5GdAd6Af8C7wpK+jNQwfmMgbcUBwPPJ0NqaVOV1V56RsKHIlzvyf++OEL7d0UMcP1Bmq1rv96Cx1t9+BSB1wC9Cgqgvduuvy2dYwCsVi8kYceBH4soj0ByckkqHMf4AfiOvii8gB7vLewFJVbQW+BiTm/lwP9Erb/rsiUutuP0JEegCvAGe5MfvBwNEd2JkQ9EYR6Ql4s3DS6zOMQDBP3ih7VPVdEbkGmCAiLcDUDMV+DdwIzHCFfj7weRzP+mERORMYT5v3PwNoFpHpwN3ATTgZN2+7268ETgUexQkXvQO8D0zowM41InKHW3Y+8JZn9d3AbSKyGThUVTf7OgmGkQVreDUMw4gxFq4xDMOIMRauMYwQEJFHgV3SFv88veHWMMLGwjWGYRgxxsI1hmEYMcZE3jAMI8aYyBuGYcQYE3nDMIwYYyJvGIYRY/4flkt+V7YFffkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#切出一天的数据，绘制一天时候的接口调用情况\n",
    "df['2018-11-01']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2=df['2018-11-01']\n",
    "#用一小时的平均值取样\n",
    "df2[['count']].resample('1H').mean()\n",
    "df2['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%#pylab` not found.\n"
     ]
    }
   ],
   "source": [
    "%#pylab\n",
    "#折线图和直方图\n",
    "#可以看到业务的高峰在什么时段，但是分不清具体时间\n",
    "#柱状图可以\n",
    "plt.figure(figsize(10,3))\n",
    "df2['count'].plot(kind='bar')\n",
    "#文字旋转角度\n",
    "plt.xticks(rotation=60)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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16kTEB4BRYGtK6UxEXA4cAP4ppXQgIm4HvgZ8rMqhrgc+wPkrjI9Ujve1iNgJDDgCV7s5AtdqdAPw8GzAppReB/4E+Hbl/W8C22o4zo9TSqdSSv8HPAP0tKBWqWEGuFajYJEfVltg9v1zVP47iPO3sr943j5n572ewf9j1QpjgGs1OgTcEhHvBahMoUwBt1be/yvgcOX1ceCPKq9vAi6q4fhvAJc2q1ipUY4otOqklJ6PiDHgqYiYAf4T+Dxwf0T8LfAa8NeV3fcDj0bEjzkf/G/W0MU48K8RcTqlNND8v0CqjcsIJSlTTqFIUqYMcEnKlAEuSZkywCUpUwa4JGXKAJekTBngkpQpA1ySMvX/ZD7LqsWZtr8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分析异常时段\n",
    "df['2018-11-01'][['count']].boxplot(showmeans=True,meanline=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>tes_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:06:21</th>\n",
       "      <td>11</td>\n",
       "      <td>2783.48</td>\n",
       "      <td>99.24</td>\n",
       "      <td>489.90</td>\n",
       "      <td>253.0</td>\n",
       "      <td>2019-05-30 23:06:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:07:21</th>\n",
       "      <td>10</td>\n",
       "      <td>1951.10</td>\n",
       "      <td>85.37</td>\n",
       "      <td>529.51</td>\n",
       "      <td>195.0</td>\n",
       "      <td>2019-05-30 23:07:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:08:21</th>\n",
       "      <td>3</td>\n",
       "      <td>494.17</td>\n",
       "      <td>103.95</td>\n",
       "      <td>211.47</td>\n",
       "      <td>164.0</td>\n",
       "      <td>2019-05-30 23:08:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:09:21</th>\n",
       "      <td>9</td>\n",
       "      <td>1798.28</td>\n",
       "      <td>101.11</td>\n",
       "      <td>433.30</td>\n",
       "      <td>199.0</td>\n",
       "      <td>2019-05-30 23:09:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:10:21</th>\n",
       "      <td>6</td>\n",
       "      <td>1017.97</td>\n",
       "      <td>74.45</td>\n",
       "      <td>298.97</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2019-05-30 23:10:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150294 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  tes_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "...                    ...           ...           ...           ...   \n",
       "2019-05-30 23:06:21     11       2783.48         99.24        489.90   \n",
       "2019-05-30 23:07:21     10       1951.10         85.37        529.51   \n",
       "2019-05-30 23:08:21      3        494.17        103.95        211.47   \n",
       "2019-05-30 23:09:21      9       1798.28        101.11        433.30   \n",
       "2019-05-30 23:10:21      6       1017.97         74.45        298.97   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07  \n",
       "...                           ...                  ...  \n",
       "2019-05-30 23:06:21         253.0  2019-05-30 23:06:21  \n",
       "2019-05-30 23:07:21         195.0  2019-05-30 23:07:21  \n",
       "2019-05-30 23:08:21         164.0  2019-05-30 23:08:21  \n",
       "2019-05-30 23:09:21         199.0  2019-05-30 23:09:21  \n",
       "2019-05-30 23:10:21         169.0  2019-05-30 23:10:21  \n",
       "\n",
       "[150294 rows x 6 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['count']>2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123cedf0>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#某一天的响应时间，平均响应时间\n",
    "df['2018-11-08']['res_time_avg'].plot()\n",
    "\n",
    "df['2018-11-08'][['res_time_avg','tes_time_sum','res_time_min','res_time_max']].plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\administrator.desktop-0hpsd3a\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>tes_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-08 00:00:21</th>\n",
       "      <td>5</td>\n",
       "      <td>763.17</td>\n",
       "      <td>107.29</td>\n",
       "      <td>269.05</td>\n",
       "      <td>152.0</td>\n",
       "      <td>2018-11-08 00:00:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 00:01:21</th>\n",
       "      <td>1</td>\n",
       "      <td>119.63</td>\n",
       "      <td>119.63</td>\n",
       "      <td>119.63</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2018-11-08 00:01:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 00:02:21</th>\n",
       "      <td>3</td>\n",
       "      <td>555.00</td>\n",
       "      <td>174.69</td>\n",
       "      <td>190.80</td>\n",
       "      <td>185.0</td>\n",
       "      <td>2018-11-08 00:02:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 00:03:21</th>\n",
       "      <td>5</td>\n",
       "      <td>977.53</td>\n",
       "      <td>105.29</td>\n",
       "      <td>267.30</td>\n",
       "      <td>195.0</td>\n",
       "      <td>2018-11-08 00:03:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 00:04:21</th>\n",
       "      <td>6</td>\n",
       "      <td>1024.15</td>\n",
       "      <td>89.28</td>\n",
       "      <td>293.51</td>\n",
       "      <td>170.0</td>\n",
       "      <td>2018-11-08 00:04:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 23:55:23</th>\n",
       "      <td>10</td>\n",
       "      <td>1419.46</td>\n",
       "      <td>95.17</td>\n",
       "      <td>192.85</td>\n",
       "      <td>141.0</td>\n",
       "      <td>2018-11-08 23:55:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 23:56:23</th>\n",
       "      <td>6</td>\n",
       "      <td>955.33</td>\n",
       "      <td>91.71</td>\n",
       "      <td>253.84</td>\n",
       "      <td>159.0</td>\n",
       "      <td>2018-11-08 23:56:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 23:57:23</th>\n",
       "      <td>6</td>\n",
       "      <td>842.42</td>\n",
       "      <td>76.82</td>\n",
       "      <td>231.67</td>\n",
       "      <td>140.0</td>\n",
       "      <td>2018-11-08 23:57:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 23:58:23</th>\n",
       "      <td>2</td>\n",
       "      <td>255.79</td>\n",
       "      <td>75.41</td>\n",
       "      <td>180.38</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2018-11-08 23:58:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 23:59:23</th>\n",
       "      <td>2</td>\n",
       "      <td>206.60</td>\n",
       "      <td>103.15</td>\n",
       "      <td>103.45</td>\n",
       "      <td>103.0</td>\n",
       "      <td>2018-11-08 23:59:23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>850 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  tes_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-08 00:00:21      5        763.17        107.29        269.05   \n",
       "2018-11-08 00:01:21      1        119.63        119.63        119.63   \n",
       "2018-11-08 00:02:21      3        555.00        174.69        190.80   \n",
       "2018-11-08 00:03:21      5        977.53        105.29        267.30   \n",
       "2018-11-08 00:04:21      6       1024.15         89.28        293.51   \n",
       "...                    ...           ...           ...           ...   \n",
       "2018-11-08 23:55:23     10       1419.46         95.17        192.85   \n",
       "2018-11-08 23:56:23      6        955.33         91.71        253.84   \n",
       "2018-11-08 23:57:23      6        842.42         76.82        231.67   \n",
       "2018-11-08 23:58:23      2        255.79         75.41        180.38   \n",
       "2018-11-08 23:59:23      2        206.60        103.15        103.45   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2018-11-08 00:00:21         152.0  2018-11-08 00:00:21  \n",
       "2018-11-08 00:01:21         119.0  2018-11-08 00:01:21  \n",
       "2018-11-08 00:02:21         185.0  2018-11-08 00:02:21  \n",
       "2018-11-08 00:03:21         195.0  2018-11-08 00:03:21  \n",
       "2018-11-08 00:04:21         170.0  2018-11-08 00:04:21  \n",
       "...                           ...                  ...  \n",
       "2018-11-08 23:55:23         141.0  2018-11-08 23:55:23  \n",
       "2018-11-08 23:56:23         159.0  2018-11-08 23:56:23  \n",
       "2018-11-08 23:57:23         140.0  2018-11-08 23:57:23  \n",
       "2018-11-08 23:58:23         127.0  2018-11-08 23:58:23  \n",
       "2018-11-08 23:59:23         103.0  2018-11-08 23:59:23  \n",
       "\n",
       "[850 rows x 6 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df['2018-11-08']\n",
    "df2[df['res_time_avg']>100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1318ab50>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kciy4cRTKpmpfG9tTMosN+/32GOi5wLMOdOSLCDzAJxuCPTeCinyISeXx0unYbU7Egl2Y4DkXDluQCWLYS0U+FEicT2mWlMMtcD+h1aA4hYp8AInXmojXAkynUehUCzyXbxZOHEFt0StOoSIfNnJYXL0kHVGOt4lWX/ZI9VAH9b6rIh8CnBSGXG6Bp0pL8VlthQeDXKkmFfmQkOkJmysnvN/RG0RukY361jz5gPCzl5bw2abd7NhfmXDSgl+/0ZgjPGdty8PTrihsHHK2YOIUPrt7ApDchAiKxSMzWk43ba1fAjS+/6iormPcfe/zyLdH8/rnVjrsg9PW8OOzDuef8zY7ltYazbqdBziiXzfHyw0Kj85oecz4Hfsr4y4vr6pl1L3THbWjtKwKgANV7qW4aks+ILz+eSGFeyviinA6AZZ/ztvc5PviLelNQpHLvDC/+Rjy6bB1TzkHq+t4cNoadu6vavLbf7+5kr3lNY7sJ5pXHOhbkWtsKi13vMzPNu1ufaUMUZFXXCH3IvuaG6/4ExX5kOA3ofCZOUoMQezUo6SHinwISCchRi9y79EboaKjUCqu4faIiHoLSR3NXlXcQEU+NGi7MKhkewhiJbdQkQ8BGnpRUkWfGnIHzZMPCam8eP1kQ0mcG4OzrcmpK4ocLS+MbCkt5+9zN5LftT0A5dV1TX5PJtc+FV5euNXR8vzCppKD/Oj5xQhQ0Luz1+YkTcHEKdx78UjX96MiHwJEUhP5n720hDNH9HXPIKCypt7V8v1GutlN901ZTe+u1oQj2/ZUOGhRc37+yjJXy/eKJ2atZ3XRfgBW2f/dwI3hszeXOJ97H4uGaxRFCTTZSh92Y1ynbLyPUZEPCfryTkkFDcnnDiryISHT1ozfOlPlFnrwMyHIDZxsXHeOiLyIPC0iu0RkRdSyXiLyvoiss/8f4sS+lOZoq8x7giYzocquCdrBzzJOteSfAc6PWTYRmGmMOQKYaX9XfIAg4brIFUVpEUdE3hgzB4gdTu1i4Fn787PAJU7sS4lPqiMExz4mamNIUbJPNia8dzMm388YUwRg/3c3Zy9kVNbUccfLSykpq2p13WfnbeHVDIeO/dHzizPaPhF/nNny2N1B45MNJfxx5jreXd60H8Dug9Vpl1lSlv62SvYoTeJaTJWP1pc4XmYsvsiTF5EbgBsAhgwZ4rE1/uDNJYW8smgbAjx4xShHyzYYDla7N0lBLA+9v5afTDgia/tzk+/8bb7XJigxZOsp9JMNpY6XuaH4oONlxuJmS36niAwAsP/vamlFY8wkY8w4Y8y4Pn36uGiSApGYvAbllXCQjZBHkHFT5N8CrrU/Xwu86eK+Qoeb49EYjGbkKEqO4FQK5YvAPOBIEdkmItcDDwBfE5F1wNfs74pP0IZ8bhOmQe20HZ8YR2LyxpirWvhpghPlK4qiKOmhPV59jhutFGO0A1Wuo09yuYOKvF9x8SIUcWewJUXxAn3vmhhfpFAqzVm8ZU/D58kOjwNeXVvP658XOlpm2KmvN/zkxc+9NkOJg2p8YrQl71P+vcASdmPgFw6PA76nvMbR8nKBhVv2MGW5ToSiBA8VeUVJgtr6cE2CEqZgnebJJ0ZFXlEUJcSoyCuKooQYFXmfE+QJEcJEmDoPhQ29QhKjIq8ouYim0OYMKvKKogQbbconJFQiP3nBVuZtKOWXryxjddF+r81xhKracGV1BJWwNXwfD9EY/2EJaf597kZq65y/3kPTGapoXwW/eLUxn/ylhVvZ/MBFHlrkDFOWaW62ouQC901ZTbs2eVx7SoGj5YamJV9bF467uaIouYsbk/mERuQVRVGU5qjIK0qOEpaeoiFxA3AnVVdFXlGSIGTvXYHwiGNY/AB3XvCryCtKjlIfJnUMCW40JlTkFSVHqVOR9x3aklcCy6rt+1m1Pbh9F8I4ycrKANdHNGHJkweNySsB5sLH53Lh43O9NkOJ4rI/f+K1CUoWCI3Ih7ChpShKEoQp6qThGkXxCG1EKEFFRV5RlEATooa8K6jIK4qi+AQ3XvCryCuKovgEzZNXFEWJQV+8JiY0Qw0rilsYY/h0Q6nXZrjCoi17aNdG6Ny+LYf37eq1OSnz4ZpdzFi902szfE1oRP75+V96bYISUl5etI2H3l/rtRmucPlfGnPlgzj/wvf+scBrExxFwzUJ2Fxy0GsTHMPPowPedOZwr03IOlt3l3ttQkL+es0JDZ833n8h7916uofWKJmgL14T4GNdTBk/+9ImBxPG/e5xXlSd5OUJndq18dAaJRO0M1QCwjR+RXg8URQlFTRckyP4OVyTgw35HHVaCQuuv3gVkc3AAaAOqDXGjHNjPz7WxZTxsyu5KHd+99nPjQIlRVxoUGQru+YsY0yJmzuoD9F5rtesouQmGq5JwIbiMq9NcIxVReEY5zsM7CuvobisymszFCVtsiHyBpguIotE5IZ4K4jIDSKyUEQWFhcXp7WTTSFJoayqreOSJz722oyWiXqcPOfovh4akh3G/+9MXghIH4zTDu8NQM9O7T22JDt8tM7V4IAnBLXH66nGmO0i0hd4X0S+MMbMiV7BGDMJmAQwbty4nA5W1Nb5y/1FvzqHTu3bMPHV5by1dDsAi3/9Napr6+nVpT0jfvWuxxa6S0VNndcmNGH0oT159Nuj6dAujyv/Oo+tuyswwNL/PpeO7a02W4/O7bw1Mks4/fQ+ol9X1u7MvMwTDjuERVv2pLVtIGeGMsZst//vAl4HTnR7n0HGb4kc+V070Ll9W4b27gJYMcNeXdrTv0dH2rcNTbQvMBwzsDsFvbswoEcnjuzXvWF5j87t6NA2t/Lj8xy+Vrp2cKbNe8yA7q2v1AKBy5MXkS4i0i3yGTgXWOHmPhV38NfzRe7iszaAt/itRWSTSZ8dNzxyO1zTD3jd7qrbFnjBGPOey/sMNH7PrPHpdZUzRJ8ekbrw+znjFk635P1A4GLyxpiNwCg39xE2fHu95qqSKL7Fjfh1GNGgqs/we8cWvbC8RY9+I6FsyQfxxauX1AWwh5RfTXbKrC2lB1mt/QAcIYQalzT19Yb1u/zZNyajdlrQXrx6zZ2vLfPahJT56+wNXpsQlxH9utn/m08s0a1j8lG/Mx78kAsem8u7y4scsy2XODoqc2PUoT0BGNSzk1fmeMajM9fx9482OVrm+GH5jpbnF0It8pMXbvPahJR5f1Xms9w8c91X+M03jnHAmka+MWog795yOhccN6DJ8tk/P5NP75yQcnnzN+12yrSc4j9PGtLw+aYzhjPt1q9y3OAeCbf55qiBbpuVdaat2JHWdt+IORZv/PjUhs93nHtkRjZdPd6qm+iG/Gd3T2D6z77Kw1eOYsHd5zQsn/Pzs3jv1tN56tqmQ3mNHXJIRjbEI9QiH0TqHIjJn3lkX647dagD1jTl6Dj5v4fld6FLGvnFmqWTPNH52xIzdvyR/bu1un3bNtY2F8XcoINMuudP765NewOPtp+GwDqemXBk/+bXR99uHRnRrxuXjR1Mn24dGpYPye/MUf27M+Hofk3X794htoiMUZH3GfV+Dco7jL7AVTLBjRmUnCITy3SAshwgRzReW/JeEKJj7mdXMrmE83T6v/ATxIwgxV2cSqv1szCGASeOb+CGNVBSp97nefJOoYKTRexTys8hjlTx81WSySWsefJpEIlx19cbjDG+7mxkjKGmrt5rM7KC30Z39CPZ1mQ/XxvRZPLeys13QX69h4Ze5IfdNZWHpq9h2F1TGXrnVL7+x4+8NikuxhiG3jmVkrLqjMqJfoPvZ56f/2Vo5gBIhsgonqnQtb0zo45EMnBas2HonVMd2Z+bvL9qJ8Pumpp2h7ohvdzrU9C/e0cACvI7Ay33X4j8Ho/AjV3jF/74wfqGzyu3+7O3ZbqNqJ+dM4JHZqwF4LD8zrx60ykNv710w3i+PenTlMqbdM0J9Ozcniv/Oi+l7eb+4ixq6uppkyec8eCHSW1TuKciLfELGk9ePZaThuYz5rfvAzDjtjPo2C6PzSXltG+b1+xYX3/aUJ76aBMDe3Zizc4DaYUmfnjGcJ6cvYGRA7tz4xnDGTmwByccdgiPz1zngEfeMW1levnxEb57cgFnHtmXXQeq6N7Jkr9IfSTihe+fxHf+Ph+Aq04cwoufWRPJvPOT0xoajhOO7se/rj+JU4bnc+rhvRnQo2Ozct6++TQGHRJf/B/59ig6tnN+uOicEPkgkMqF3K6NUGNPLnLy8HwemWEtP2V4Pr27Nrbk87um3qo/d2T/lLcBOLRXY+vkmAHdc3YKw/svPY67Xl/e8H1Y7y6cf2zT/PTD+1q9hgcfEr9FF2kRZtKqi+SDR3pxnnZEb6pqgx8iy3S8mrw8oaB3FwqiGheR+kjEKfasWwCHRE3Kcuygph3RTjuid9zlERJ1XLt0zOBW7UiH0IdrgkIq8dCAhE5bxa8xTK+JvHx3I50u6OgxSZ2cEPkgjFbnhm7r9RBMIueC0/UXhg5omfZKzUVyQuTbtvG/m6m0zkPSkA8lTghz5FzIpNUalqe9WFTjU8f/6ucAbQNwZmQyZVgjkuCbkirppBQ6ccwbwzUROxwoNCT4IVwTtOrICZFvEwSRT6UlH5Krvt73/Ra82q+14zB1XnIKPSKpkxMif6Cy1msTmrFs215Oun8Gu/ZXMvre6fz6jeTnN4/Wni4dGlOuYnPkk03HimRzOMXgFlLEYrnmqc+4ffLShu9Pzt7A6Hunc/0zC/jbnI0s37Yvq8M81NTVUzBxClc8+QkFE6ewt6Im5TJ6RmVeAAzu1XJOdEtERvWMZCwdliCvuiUO6WJl10RnWyXT1imYOIWCiVM4/Q8fpLzPbPDsvC1pbzskjbqIR+80stZao0endq2vlCaaQukRry7axs79VRTurWBveQ0vL0p97PsXfzCekQN78MerxrCvooZvf+XQJr8P7NmJJ68eyw//tbjFMs4b2Y/7LjmON5cUNkxCkSn/d+Uojr9nelLrvvZ5IQ9/ezQA763YQbs2eWwqOcjML3YB1oQkJw3txfhh+Ywfls/RA7q79mR2sMpqDCzYvAeApVv3JrXdZWMHMWdtCdedWsB5I/vz9s2nMfOLnQzo0ZHzRzamT75186m0i/N+6Ii+XVkXNcvR6EN78serxjDh6L5cMnogow7tyY59lSmFKi4bM4g8aTqWfNs2eTz9vXH81zMLW91+6+6KpPflJ04els+8jaVNlr1982nMXV/Mt8a2nqL4wg9O4mBVHS8t+JIZq3dx+djBXDLGOoYv//BkenftwJBenXlh/hZ+dZE1Z8NLN4ynXwYNpeeuP5HhfVpP40wXFXkPMMYw3Z4cpDaDluoJh1kTDMROhBDN+ccO4IoTBje5iRzSuR17yq1W6vWnDaNPtw58//RhadsRS/eOja2Szu3bUF7den52ZU0dK7fv4/unD+OX5x/Fzv2VfLqxlE837mb+xlJmrHZf9Ktrmw4psX1fc6Hb/MBFFEyc0mTZdacM5eErRzd8P25wj7j50McPjn8TPfXw3k1EHhrrNDLeeKqtx7w84bI4onb2Uf3irB0e7jhvBLf8ewnb9jTWXUv1EY9Thlt57qu272fG6l0M7NmR04/oA8BXCno1rDfz9jMbPp+U4YxSkfLdQkXeA1YU7qdoXyUAtXUZjMORpLbF7iGboeZk5Xd54T5q6gwn2DPj9OvekYtHD+Li0YMA2LGvkvmbShuEPyL63Tu25cSh+Ywf1ouTh+dzdP/uaafZVdY0FfktpeVplZMqfn4vETycueGH6XWIirwHRHfNrq1PfUCyiCaE6Dxk0RYrRDL2sPjTn/Xv0Vz0LcG3/mastp6MenRqx4kNLf1eKYl+ZUyP0A1JThQdJkFQmhKG+6+KvAdMX7WDbh3bcqCyNqNwTbrZF9EnrtutyGTjyIu27GFY7y706tK+9ZWxRP+SMYO4ZIwl+kX7Kpi/cTfzNpTy6abShrlye3Rq1yS8c1T/bi2KfmXMyJgbipMT+UwJgY74BqduuGG6b6vIZ5lNJQdZu7OMy8YM4rXPCzML1zhol2skYaQxhsVb9nDWUX3T3s2AHp2aiP72vRVWeGfDbj7dVNrwDqRn53acWGCFdsYPy+fIfo2iH1Z8fHIAAA6RSURBVBuu2Zy1cE1WdqPkKDkp8vX1xrPu0dPtUM0Fxw3gtc8LqUsjXBMh3VZLdOvdD/qyubSc0oPVDS+SnWBgz05cOmZww6BPhXsrmL+xMaYfLfqRlr5XN01nOsIpbhCGuslJkT/hvvc962hSVlnLyIHdGdrbytm94+VlKZfRu2t7Ssqqk/ahW8em1dy7awf2230H2rd1t6tE324dWu2n8E17qNaxQ5wT+VgG9ezEZWMHN2ScFO6t4NMNtuhvKmXayp1plx0vLTIVunZomiOdaXlOMdYeGjlItG+TR36X9k2ya9Ih0lch8j/IBN8Dmzk/P4uvPjir2fL2bfMaUuOOH9yDdm3yOGZA92yb14SLjh/A0N5d+enZh7OnvIblhfvo3L4NS7bubZJueP7I/vTo1I6rxx9G4d4KHp2xlpvOHM7YIYfw2abdSe/vF+cdxcAenbhy3KGc9+gc3vnpaawo3M//TV/DmBZy42fefkZGY+8//b1x5HfpQP8eHTnp/plx17ng2P70696RunpD/x4dGdHPvVzhWAb17MTlJwzm8hMs0d+2p5z5G3fzxpJCDu3VmX7dOlJSVsVH60vYVHKQUYf2ZJz9pDHt1q+yvHAff561nvHD8xsm5UiXW885gl5d2jFyYA+emLWekQPdPT8fvnIUt01eyoXH9WfdzrIm6Zu9u3agpKyKU4bnu5q7nS57yqt5Z1kRt39tBB+uLW54YX/RcQPo3qktIwd252/fHcdTH28CY/U5SIdrTj6Mmrp6rjt1qJPme4L4LX1r3LhxZuHC1jtrKIqiKI2IyCJjzLjY5f54LlQURVFcQUVeURQlxLgu8iJyvoisEZH1IjLR7f0piqIojbgq8iLSBngCuAA4BrhKRI5xc5+KoihKI2635E8E1htjNhpjqoF/Axe7vE9FURTFxm2RHwRsjfq+zV6mKIqiZAG3RT5eb51mOZsicoOILBSRhcXFxS6bpCiKkju43RlqGxA9k8VgYHvsSsaYScAkABEpFpF0p3/pDZSkua3fUF/8R1j8APXFr2Tiy2HxFrraGUpE2gJrgQlAIbAA+I4xZqVL+1sYrzNAEFFf/EdY/AD1xa+44YurLXljTK2I3AxMA9oAT7sl8IqiKEpzXB+7xhgzFZjq9n4URVGU5oStx+skrw1wEPXFf4TFD1Bf/IrjvvhugDJFURTFOcLWklcURVGiCJTIi8gA8Wq2D4cRkX5e2+AUYakXrRN/ovWSGYEQeRHpICJ/AWYDk0TkMq9tShcR6SoijwDvishfA+5LKOpF68SfaL04QyBEHvgmMMAYMwJ4B7hXREZ4bFPKiMgg4Dms434hVoX/wVOjMiPw9aJ14k+0XpzDtyIvItFzjxmgGMAY8ybwHnCjiKQ3t5d3VAJ/N8bcYozZAUwGlojI8R7blTQhrBetE3+i9eIQvhN5ETlcRCYDz4jIRSLSBagA9ovIQHu1B4GxwEh7G1/GHkXkSBF5UkQ6ARhjSoEPo1Y5FBgGrPHAvJQIS71onfivTkDrxc168ZXIi0ge8CiwHOtR7evAr4EZwFHAKBFpb4zZifX49jMA48M8UBE5DcuHG4Db7GVijDkYtVp7YLMxpsoDE5MmLPWideK/OgGtF3C3Xnwl8sAAYC9wv/1Icx9wNjAaeAa40v4M1tj0e0SknQd2JkMp8F/ACOA6ETksTkWOATYAiMgPfPwoGpZ60TrxJ1ovLuK5yEc/phhjCoFxwNeivv8Z+B9jzMtYg53dKSK3Yx2gjcaYmuxbHZ8YX1ZjTZiyHngfuNdeJ/qYTwDyReRV4DtYcUjfEfR6iaB14r86Aa0X1+vFGJP1P6CvfSAkallb+//3gI+ilvfEeunyFazx6U8HHgOu9sL2ZHyJ+i3So7gbsB6YEPP7u8BK4Fte+2Hbkw90j1kWuHqJ50fA6+SQoNdJS74EuF46xVnWxo/14sXBuduuxGnAE9EVHDlQwAfArVHLngWO9bpiU/UlpuJvBd6xP19lV/aZXvsQZeddWI/DTwO/iPN7IOqlNT8CVie/AoqAl4HfBLVOkvElYPXyAPA2MMb+nufnesn2wTkfmI4VJmqLNalIP/s3ofFufgKwCbgEuBpYBBzjdeUm60vMetE3sD3APuApoKPXPkTZda59o2qHNWnBTOA/gfb273lBqJfW/AhYnRyDlWbXEWsyiNm24HUJUp0k40vA6uX7wOfAw8BvE6znm3pxPSYvIr2ivuYBB7FE8VisSUSOBuvtsjHGiEgbY8wirLv5GKw37rcYY1a5bWtrJOtLNLZPPUTkQaz5bi80xlxvjPE0phjjS3esWGEbY0wJ1kl8C/YLImNMvYjk+bFeUvEjgl/rJIYyrMf8rsaYLcBfgDOA48HfdRKHhL5ECEi9vIKVMfMG0FdELoSm7+N8Vy8u3vF6AH8HPgF+DAzHevP8GFZ8rRi4B1iClTbV194uzy2bsu1L1PZ5wPFe+9GCLwXAqVhDnF6Llar2KDAFuN7epuEpyy9/6frh0zrJt+0+I2rZkcAjwNlRdfA48BMawxq+qpNMfAlQvUSeoHpgNSAex37/Q2Mkwlf14mZL/k6s2NT1QC/gH8aYImPMLcA6rJcO99gVPQHoDFYLxUWb0iUtXyIYY+qNMcuya3KLxPPlY6zQxnnAHOBLrMfka6DxKcsbc1skLT8iG/ulTkTkMKyb1eXAVSKSb/+0HqgBxojIQNv2aVjTZ9aB/3LeM/Elgo/rpRc06pMxZh8wD+uG9S17mYn+7xccF3mxiMw49bwxZrUx5rdAnYjcZy8vpVHU59qfezUvzVtyxJc8EfmVMeYl4EfAxcaYh7HiphvFGljJN70kw+JHFGXA74ChWPHqs0WknS1+7wCHA+cAGGOmAGVRvSb9Rph9mRBJ6Yw6j1YAs4DjROTnInKTH88xx0XebvTVAgOxXj5E+BFwg93Ftxy4XERuE5GXsV6wbHHalkzJEV9+CNwiIr2MMXuNMTtFZAxWb71CY0yVn1omYfEjgrG67680xuwHXsR6ITnA/m0O1sv9b4nIIyIyA9gJ7PLK3kTkgC+D7N+MHXcvx2rJ/wdW3H2jH88xN+JYkZjVWKxYdaeo354GbsJ6y34h1gX4I6dtUF9S9uVvwM/tz8cAq4hK//LTX1j8SODfK1hd3dtFLSvAetdzk9f2qS+N7wyx4vIbgTu8ti/RX9rT/4nIL4E64DET1WPLfqQRY0ydiPwbqDDGXGf/dgewwxjzr8i6xgcxePWl0Rf7cbOdMabaC/uj7A2FH7ZdLfoSOWfsrLI6ETkDuB1LCL8KzDLGbPLC7nioLw2+zDbGbBBrHBrPz7GEpHFH64jV4aQQWAiMivotOnthKNABmGuv/x/AMuAyr+9s6ktcXy732ocw+ZGiL8fHbPcBsB9rUKvBXvuhvsT3JXpdP/+lc4AEK1WtDXA/VqeAblG/9wf+CXyK1SllNNbgQ9P9dAGqL/70JSx+pOjLXKx3DB2wusRvAS7x2n71xf++JOVvEgekLXAHcGjUso72/wFYb5fPjdzV7M++jLepL/7zJSx+OOULMMRrP9QX//qSlv+tHJzjgMVYb8FfjPktckBuBV4ABsbZvo0TRjpU0eqLz3wJix8O+dLWax/UF3/7ku5faymUJVg9uo4CCkTkXLBeRkSt8wTQCRgnImeIyKX2OmJiOjp4jPqC73wJix+QuS+12TY4AeoLvvQlPZK4E3ay/98IfBh9F6Qxne0HWOM7r8fHMSv1xXu7w+qH+qK++PUvpQOFNZLcT2OWj8LKR37Aa2fUl+D6EhY/1Bf//oXJl5T8TvEgnQfMtz8fi9UZoDuQ77UjaVS4+uKzv7D4ob749y9MviT7l9KwBsaYaVhzElYBvwc6GGP2G6sLcKBQX/xHWPwA9cWvhMmXZEla5EUkT6xBuY4GbjbGXGSM8eW4E62hvviPsPgB6otfCZMvqZDSsAYicgHwgTGmyj2TsoP64j/C4geoL34lTL4kS9pj1yiKoij+x/Xp/xRFURTvUJFXFEUJMSryiqIoIUZFXlEUJcSoyCuKooQYFXlFUZQQoyKvKHEQkQIR+U4a2z0jIt9KY7vvicjAVLdTlNZQkVdCj4i0TWOzAiBlkc+A72HNQqQojqIir4QCEfmuiCwTkaUi8pzdon5YRGYBvxeRLiLytIgsEJHPReRie7sCEZkrIovtv1PsIh8ATheRJSLyMxFpIyIP2tsvE5Eb7e1FRP4kIqtEZArQtxU7/9suY4WITLK3/xYwDnje3l8n946Ukmtoj1cl8IjISOA14FRjTImI9MKat7M3cLExpk5E7gdWGWP+JSI9gc+AMYAB6o0xlSJyBNbsQeNE5EzgDmPM1+193AD0NcbcJyIdgI+BK+wybgLOB/phDVn7fWPMKy3Y2ssYs9v+/Bww2Rjztoh8aO9voQuHSMlh0nmMVRS/cTbwijGmBMAYs1tEAF42jbNHnQt8U0TusL93BIYA24E/ichooA4Y0cI+zgWOj4q39wCOAL6KdWOoA7aLyAet2HqWiPwC6Az0AlYCb6fkraKkgIq8EgYEq0Uey8GYdS43xqxpsqHIPVjzf47CCl9WJtjHT+yhaqO3v7CFfTcvQKQj8GdgnDFmq73vjslsqyjpojF5JQzMBK4UkXywQiJx1pkG/ETsJr6IjLGX9wCKjDH1wDVAZO7PA0C3mO1vEpF29vYjRKQLMAf4DztmPwA4K4GdEUEvEZGuQHQWTuz+FMURtCWvBB5jzEoR+R0wW0TqgM/jrPZb4FFgmS30m4GvY7WsXxWRK4BZNLb+lwG1IrIUeAZ4DCvjZrG9fTFwCfA6VrhoObAWmJ3Azr0i8jd73c3AgqifnwGeFJEK4GRjTEVKB0FRWkBfvCqKooQYDdcoiqKEGA3XKIoLiMjrwNCYxb+MfXGrKG6j4RpFUZQQo+EaRVGUEKMiryiKEmJU5BVFUUKMiryiKEqIUZFXFEUJMf8PbIaOI6dOH+MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2018-11-08':'2018-11-10']['count'].plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#星期三\n",
    "df['2018-11-01'].index.weekday\n",
    "#加入周末的列\n",
    "df['weekday']=df.index.weekday\n",
    "df['weekend']=df['weekday'].isin({5,6})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend\n",
       "False    7.016846\n",
       "True     7.574989\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('weekend')['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x131d89b0>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean()\n",
    "df.groupby(['weekend',df.index.hour])['count'].mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x134dda50>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bTqmUYvGY3jzz/kEOnalgUM8mztOwoCfTqIpokusmcKXUKmAq0F0plQc8hZG431RKPQicAha6JJo2Xim7S+MCVhaLhcZ1Y2pqahqea63ZuXMnfn6d7B+P8FzFh+BfT0KfKUYX9c9/YQw1zH4W+k4xOzrX2b0SrP6QPA+A7LwLHD5bya/uGe7W085Ljee3Hx1i1c5TPD23iTLXsSmgrMY4uIkJ/EZmoSzRWsdqrX211vFa61e01qVa6+la6wHOr1fPUul0LBYLkZGRHDlyBIfDwdtvv93wvRkzZvD88883vJb+msKtbLWw7kHwC4Z5L8HX/gFffwvsdfD3ubDuYag4a3aU7WerNWqfDJkDgcZSk7cyThPga2HOiFi3njoq2I9bk3vy9u58auqbaInoFwwxQ02/kSnTKlrhN7/5DbfddhvTp08nPv7y+Nvzzz/Ptm3bSElJYejQobz00ksmRik6vc9/Ydwruut5CO1hbBs4Cx7ZAZN/DAf+CX8aAztfAocX92M99CHUnG9oWlzjXCV5e3IsYQGt60LfFkvG9OZCdT0f7mvmZmZcqpHAO7Ci69WknKxJuurPLdrp2EZ4/W5IexDmPNf0PiVHjLHxE5shdqSx36UxW2+yciGc2QdP7AOLlXey8nl8dRZvPDSOif27u/30Dofmlv/ZRI+wAN5cPuHaHTJeg/ceg+9lQrd+bo1FyskK4e0ulsE/vwPdBxrd0pvTfQDc/w7MfwUqCuGl6bDhB1DdQqlUT1NxBo5+CiMWg8UoE/tWeh7xkYHNtz5zMYtF8bUxvdl5ooxjxU0sm298I9MkksCF8AZaw7vfg6oSmP8y+LXcKgylYPgCeHQXjFsOGSvgT2mwZ42pf/LfsOw1xnJ15/BJ3rmLbDtWwoLR8Vgs7S9cdaMWjI7Hx6JYs+v0td+MHgy+QaaOg3tEAu/IYRxP0NV+XuECu1+HnA0w/ecQO+LG3xcQDrf/Bh7eCBEJ8PYyeHUOlJ1wX6ztdWnud/xY468JYF1GPlrD/FT3zP1uTkxoADOG9GBdRh51tqt6YVp9jCGqrpzAAwICKC0t7TJJTWtNaWlpk13uhWhSyVH48CfQZzJMeLRtx+g1Eh78FOb8Ac5kw3uPuzZGV8rPhOIcGGWsvHQ4NGszT3NT/27XbVLsDovH9qa0qo5PDjQxsycu1ShqZW+hhrgbdVxTt2bEx8eTl5eHNy2zb6+AgIArZrEI0Sx7Pax/yKiDfc9fjbZebWWxQNq3oDwftv6PMdXw0iwWT5K1EnwCYNg9AHx1oozTZdX8cOYgU8KZNCCauIhAVu86xR0pV01fjBsN9j/B2f3Gf5IdzPQE7uvrS58+fcwOQwjPtOnXULAbFv0dwnq55pjJ82HL7+DAOzBumWuO6Sr1NbBvLQy50xj+Ad5KP01ogA+3Jfc0JSSrRbEorTe///Qwp8suXvlXQOMWayYkcNOHUIQQzcjdBlufg1H3wdC7XHfcmCHGIpT96113TFc59AHUXGi4eVlRU88H+wq5s51Ni9tr0Zh4LIprmz1EJEBQd9NmokgCF8ITVZ+H9csgqg/c5oYSE8nz4NR2uJDn+mO3x55VENrLKBEAbMgupKa+/U2L2ys2PJCpg2J4Kz0Pm73RzcyGFmvm3MiUBC6Ep9EaNjwBlWeMKYP+Ia4/xzCjtgj73255v45UcRaOfgYjvtZo7vdpBsSEMKKdTYtdYfGY3hRVGKVsrxA32rjpWlvR4TFJAhfC0+xZbQxvTP13962g7NYPeo0yytF6ir1vGnW/RxjDJ0eLKsg85bqmxe01bXAMMaH+rL56TnjcaEBDQcfXQJIELoQnKTsOH/wIEibCzU+491zJ840bpKXH3HueG6E1ZK0ykmH0QMBoWmx1YdPi9vKxWlgwOp5Nh4o4f7FRu0UTW6xJAhfCU9htxri3ssK8vzYMIzRHa83GQ0WUVbWxd6tzmp5H3Mw8kw1F+2HEEgBsjZoWR4e6pmmxK0weGI1DQ+apc5c3BkVBZB9J4EJ0aVt+B3m7jOJTEQkt7upwaJ5+dz/fWrGLmc9tZkN2QesXw4XHQ+/xsM8DEnjWKmOue/J8ADYfLqa4otZtXXfaakR8BL5Wxa7cc1d+w6QWa5LAhfAEhdmw5beQstioYdICm93Bj9bu4bXtJ1kyNoH4yEAefWM3y1/PoKi8psX3XiN5vtHx5uyBdgTfTvZ6o+73wNuMq1kuNS3245bBntUvPdDPyrBe4WQ0lcDL84wiXB1IErgQniBjhdF55vbftLhbrc3Od9/IZH1mPj+YOZBf3ZPMuu9M5N9vH8zmw8XMeG4zb6WfvvGr8aF3gbKYO4xy5BO4WNLQtLi0spbPDhZxzyj3NC1ur7TESPbknafW1qjWukmVCT3v0xGiq7HVGrNBhtwJgRHN7naxzsZDr6Xzr/1n+fmcoTw2fQBKKXysFpZP6ceHj09icM8w/m1tNktX7CLv3MXrnzu0ByRNMoZRzKpHtOcNCI6G/tMBePtS02KT5343Jy0pilqbg3355Zc3Nm6x1oEkgQthtsMfGasPRyxudpcL1fXc98pOth0t4bcLUnjg5mvLT/SNDmH1svH8v7uGkZ5bxq2/38Lft+ficFwnMSfPh7JjRlGmjnaxDA59BMMXgdUXrTVrM/IY0TuCgW5qWtxeoxON9m4ZJxt1kvQNhB7DJIEL0eVkrYLQWOg7tclvl1TWsuTFHWTnnedPX09tcVWixaK4f0ISHz8xmdTESH7+zn4Wv7iDEyVVzZ9/yJ1g8TFnTvjeteCoh5HG7JN9+eXknKnwuJuXjUWH+pPULajpG5kFmeBwNP1GN5AELoSZKovh6CeQsqjJaYMF56tZ9NftHC+p5KX705g9/Maa+cZHBvH3B8by2wUp5Jwp57Y/bOHFLceuXAZ+SVAU9JtmrMrswOQDGMMnPYZDT6PL/LrMPPx8LNyZ4qLCXW6SlhRF5slzV95riBtt/CVVdrzD4pAELoSZ9q0Dh82YfXKVEyVVLHxhO8Xltfz9gXFMHdS6GRlKGVX0Pv3BFCYPjOZXH+Qw/y9fcuhME0u+k+fDhdPGNMaOUpRjLCRyXn3X2Ry8u6eAmUN6EB7k/qbF7ZGWGElpVd2Vf9k0rkzYQSSBC2GmPauMDjs9hl6xOedMOQtf2E51vZ1Vy8Yztk9Um08RExbAi/eN5v+WjCLvXDVz/m8r6zKuKmI1aLYxC6Yjh1H2vGHc+Bu+EIBNzkVJ80d7xsrLlqQlGePg6Y2HUaIHgW+wJHAhuoSig1CY1bD68JLdp87xtb/uwGqBN5ePJzmu/YWclFLcOaIXn/xgCslx4fz3RzlXtggLCIOBs5zDKPbmD+QqDjtkvwkDZkKI8ZfF+sx8uof4MXlAtPvP3079okOIDPIlvfGNTIvVqC8jCVyILmDPKuPmYfLlhTtfHivh3pe/IjzQl7Xfnkj/GNfOxIgK9uOx6QMorqjlw32FV34zeT5UFUHuFy49Z5OOb4SKwob/vM5V1fFZzlnuGhmHjwfO/b6aUorRiZGkn7z6RmaqURbA1sbyBq3k+Z+UEJ3RpSvQ/jMhxLji/PTAWb65YhfxkYG89e0Jbuv/OGVANH26B/Pql7lXfmPArcYQQEcs6slaBQERMOh2ADZkF1Bv1x3etLg9RidGcby4itLK2ssb40aDvQ7O7uuQGCSBC2GGE5udV6DGzcuMk+dY/o8MBvcMZc2yCfQIc1/Ta4tFcd/4RHafOk923vnL3/ALgsGzjVZr7mzSW3MBcjYYV/w+RqGqtZn5DO4ZytBeYe47r4uNSbo0H7zRVXgH38iUBC6EGfasNno+DrwNgD9vPEp4oC8rHxpHZLCf20+/IC2eID/rtVfhyfOh+hwc3+S+k+//J9hqGtqmHS2qZM/p8yzw4LnfTUmOC8fParlyGCU8HoJjOmxJfbsSuFLqCaXUfqXUPqXUKqWU+y4bhOgsaivg4HtGVxzfAI4WVfJZThH3T0gkNKBjps+FBfgyPzWeDXsKKWk8BNBvGviHu3c2yp5V0G1Aw9Xq+kyj7vfckZ499/tqAb5WhseHk57b6EZmB7dYa3MCV0rFAY8BaVrrZMAKNL8WWAhhOPAu1F9suAJ95YsT+PtYuG98YoeGsXRiInV2B6t3NmrU6+NvrMw8uMHoEO9qZceNXpwjl4BS2B2at3fnM3lAd2JCve/6Ly0pkr35F6ipv6qwVclhY6jIzdo7hOIDBCqlfIAgoKD9IQnRye1ZBVF9IX4MJZW1rM/MY15qPN1COrZxQf+YUG7u351/7DhFfeMVmsnzoK7CWCHqantWA6ph4dKO46UUXqhhvpcNn1ySlhhFvV2TndcoWcel0lEt1tqcwLXW+cCzwCmgELigtf7YVYEJ0SmdPwW5W43pc0rx+vaT1NocPNhEcaqOsHRiEmfKa/h4/9nLG/tMgaBurh9GcTiM/7z6ToFwY7HOuow8QgN8mDGkh2vP1UEuFba6Yj54r1HG1w4YRmnPEEokcBfQB+gFBCulvtHEfsuUUulKqfTi4uK2RypEZ5D9pvE1ZRE19XZe33GS6YNj6B/jhs7zN2Da4BjiIwN5rfHNTKsPDL3bqBJYW+m6k5360vgPzNm0uKrWxof7zjAnpRcBvi23j/NUUcF+9IsOvnJFZlAURPXz7AQOzABOaK2Ltdb1wHpg4tU7aa1f1Fqnaa3ToqM9f4WVEG6jtXEFmngTRCaxPjOfsqo6HprU17SQrBbF/RMS2ZlbxoGCRvWtk+eDrdoodesqWavALwSGzAHgw31nqK63Mz/V85fOtyQtMYqMk+euLNvbQS3W2pPATwHjlVJBSikFTAcOuiYsITqh/AwoPQojluBwaF7+4jjJcWGM79v2OieusCitNwG+liuvwhMmGCVuXdUvs64KDvzTuLL3CwaM2SeJ3YIahiG8VVpSJBeq6zlW3OivlbjRUFEA5e69LdieMfCvgLVAJrDXeawXXRSXEJ3PnlXgEwBD72LjoSKOF1fx8KS+GNc/5okI8uOeUXH8Myufc5c63FssRtf6o59A9fmWD3AjDm6AusqGyoP556vZfryUeaPiTf/52ystyfgP+Ir64B3UYq1ds1C01k9prQdrrZO11vdprWuv/y4huqBLbdMGz4GAMF7aepzY8IAbru/tbksnJlFrc7Am/fTljcnzjWXhOe+3/wR73oCIBEgwRlnfzsxDa5jn5cMnAEndgugW7Hfljcyew406N24eB5eVmEJ0hCMfGyscRyxhX/4Fdhwv41s3JXlM097BPcMY1yeK17efxH5pLDdutJF02zsb5UIeHN9szLyxWNBasz4zn3F9otxW76UjKaVIS4q8ckm9bwD0SJYELkSnkLUKQnpA36m8tPU4If4+LB6bYHZUV/jmxCTyz1fz6UHnlEKljKvw45ugqqTtB85eA+iGui+7T5/neEmVVxWuup60xChOll6kqKLR4qe40UbDCjd2OZIELoS7VZXCkX9ByiIKKurZkF3I4jG9CeugZfM3aubQHvQKD7jyZmbyfNB2o8BVW2ht/OeVMNFYvIRx8zLA18Ltw3u2P2gPMfpSYaurx8Fry40b124iCVwId7vUNm3EElZsOwHAt0xauNMSH6uFe8cn8uWxUg6fdbZd65EM3Qe2fTZKfgaUHmm4eVlrs/PenkJuHdazw+q+dITkXuH4+1iauZHpvmEUSeBCuNueVdBzOBXhA1m98zSzh8cSFxFodlRNWjI2AT+fRlMKLw2jnNzWtilxWW+AT6AxfRD4/GARF6rrO9XwCYCfj4URvSPIaHwjs/sA8AuVBC6E1yo+BAWZMGIJa3adpqLWxsOTPO/q+5KoYD/mjujF+sx8LlQ7a4IPmwdoowxsa1yaeTPEmHkDRtf5HmH+3NS/u2sD9wBpiZHsLyjnYp3N2GCxQq+Rbk3gPm47shDCuPpWVmxD57PiLwcY2yeKlPgIs6Nq0TcnJrE2I4+30k8bq0SjB0KP4bDp15CxwugmpB3G2LjDcfm5dji/ZzfGvh02o+qis21aSWUtmw4V8+CkPlgt3j33uyljkqL486ZjZJ0+z8R+zv+g4kbD9ueN/8x8XF+sTBK4EO7S0DZtBh/kOsg/X83Tc4eZHdV1JceFM4hp7dkAABslSURBVDoxktd3nOSBm/pgsSiY8TTs/jsoi/NhNa4wL71ueH7V9pAY6HsLAO9mFWBzeFfbtNZITbh8I/OKBO6ohzP7IH60y88pCVwId8ndCuX56FnP8PKm4/TtHsz0wTFmR3VDlk5M4rFVu9l0uIhpg3vAgBnGox3WZeYxPC6cgT1c26jZU4QH+TKwRwi7mmux5oYELmPgQrjLntXgH84uv3Fk513ggZudV7Ne4PbknsSE+vPqlyddcrycM+XsLyj3+sJV15OWFMXuk+cuL4YK6wUhPd02Di4JXAh3qK00Ou8Mu5sXtxcSGeTrVUMHvlYL945LZMvh4iuLNLXR+sx8fCyKO0d4V9u01kpLjKSi1nZ5GqabW6xJAhfCHQ6+B/VV5CfezWc5Z7lvfCKBft5V83rJuN74Wo2mE+1hszt4e3c+twyO6fCuQx1tjLOw1RV9MuNSjbnwrigKdhVJ4EK4w55VEJnEn491x9dq4b4JSWZH1GoxoQHcMTyWtRl5VNba2nycL46WUFxR2+mHTwDiIwOJCfW/slP9pXHwgt0uP58kcCFc7UIenNjCxSELWZuZz7xRcUSHeueV59KJSVTW2liXkdfmY6zLzCciyJdbvOQGbntcKmx1RYeeuFSY/ayxotXFJIEL4Wp71wKaNXU3UWtz8JAHL9y5nlEJkYyID+e17blXdpy5QeU19Xy8/wx3pvTC38e7hpDaKi0xivzz1RReqDY2BITD2Icb+oC6kiRwIVzt2Oc4YobxfJaNWwZF0z/Gu6fNLZ2YxPHiKr442vqKhB9kF1Jrc3ht1/m2SHMWtrriKtxNZB64EK5kq4XTOzkSP5+SU3U8bGK/S1e5IyWWX31wkF9sOMC0wTFEh/objxD/hufhgb5NdtZZn5lPv+hgRsSHmxC5OYbEhhHoayU9t8zts24kgQvhSvmZYKtm5dkEhsaGMaFfN7Mjajd/HytP3jGEZ/91mBVf5lJnu7a+tZ/VQvcQP6LDAhoSe3igLztzy/i3Wwd5fdu01vC1WhiVEHHljUw3kQQuhCvlfoFG8c65JJ7+Wp9Ok7juGRXPPaPi0VpTXmOjuKLWeFTWXn7ufJ1/vpqs0+cpraol0NfaKdqmtVZaYiR/2niUylobIf7uS7OSwIVwpZNfUODfF1+fKO4Y3vkWrSilCA/0JTzQl/4xIS3ua7M7sDk0Ab5d4+ZlY6OTonBo2H3qHJMGRLvtPHITUwhXsdWhT33FptqB3DIoBj+frv3Py8dq6ZLJGyA1IQKLcv+NzK79GyaEKxVkomzVbKkb3CXmPIvmhQb4Mqhn2JWd6t1AErgQrpK7FYB0hnDzgM7XsEC0TlpiJLtPncdml6bGQni+3G0ctybRPzHB4xoWi46XlhTJxTo7OWcq3HYOSeBCuIKtDsepHWyuHcQ0GT4RGKVlAXblum8YRRK4EK5QsBuLrZodjiEy/i0AiIsIpFd4gFvng0sCF8IVnOPfp0NHMeA60+tE1zE6KYr03DK0bn0dmRshCVwIF3Cc2MohncCowf06zeId0X5piZGcLa8l71y1W47frgSulIpQSq1VSuUopQ4qpSa4KjAhvIa9Hn3qK7bbB3PLIBk+EZddKmyV4aZhlPZegf8v8JHWejAwAjjY/pCE8DIFu7Haq0lnGBP7e3/tE+E6g3uGEeLv47YbmW1eSq+UCgMmA98E0FrXAXWuCUsIL+Ic/3YkTCDIT6pTiMusFsWohAiPvALvCxQDK5RSu5VSLyulgl0UlxBeo/rIZnIcvRkzzPUdV4T3S0uM4tDZCi5U17v82O1J4D5AKvAXrfUooAr46dU7KaWWKaXSlVLpxcXF7TidEB7IXo9P/k5j+qCMf4smpCVFojVknnL9VXh7EngekKe1/sr5ei1GQr+C1vpFrXWa1jotOtp9VbmEMEVBFr72ak6EjCSpu/wBKq41KiGCP3xtJClxrm9q0eYErrU+A5xWSg1ybpoOHHBJVEJ4ifpjWwAIGTjZ5EiEpwry8+HuUXF0C3F9Y+v23nH5HrBSKeUHHAe+1f6QhPAe5TkbKXHEMy55sNmhiC6oXQlca50FpLkoFiG8i72ekKJ0PmIy8/tEmR2N6IJkJaYQbaQLduPvqKa859gu27hAmEsSuBBtVLp/IwDRydNMjkR0VZLAhWij6sObOOyIY0LKELNDEV2UJHAh2sJeT/dzu8kJGEFcRKDZ0YguShK4EG1QdTKDQF2NPeEms0MRXZgkcCHaIG/3JwAkjJphciSiK5MELkQb6BNfcEzHkTJY6p8I80gCF6KVtL2e3pVZ5EeMxtcq/4SEeeS3T4hWOrH3S4KpwbefLJ8X5pIELkQrndnzKQADxt5qciSiq5MELkQr+edv57Q1nu49E8wORXRxksCFaIXzlRcZWLuPsu5jzQ5FCEngQrRGdvpWQlU1YUNuMTsUISSBC9EaFw58DkBC6iyTIxFCErgQN8zh0EQU7eSsX2+sYT3NDkcISeBC3Kg9p0oYoQ9ysdcEs0MRApAELsQNO5C5jTBVTXSyLJ8XnkESuBA3qPboZgBCBk0xORIhDJLAhbgBxRW1JFbs5lxgAoTK+LfwDJLAhbgBmw+dYYwlB514s9mhCNFAErgQN+Bo9peEqWoih0r7NOE5JIELcR02uwPrqW0AqCS5AheeQxK4ENeRcfIco+z7qAxJgrBYs8MRooEkcCGuY1POGcZZcvDrN8nsUIS4go/ZAQjh6U4f+IpQVQ39ZPqg8CxyBS5ECwrOV9PjXLrxIkkaGAvPIglciBZsOlTMeMtB6sL7QFgvs8MR4gqSwIVowcaDhYy35uDbV8a/hedpdwJXSlmVUruVUhtcEZAQnqKq1kbJ0QxCuYjqIwlceB5XXIE/Dhx0wXGE8Cif5RSRqvcbLxJl/Ft4nnYlcKVUPHAH8LJrwhHCc3y4t5ApfjnoqL4QHmd2OEJco71X4H8Afgw4XBCLEB7jYp2NnYdymUA2qt90s8MRokltTuBKqTlAkdY64zr7LVNKpSul0ouLi9t6OiE61Oc5RUyx78RX18HwhWaHI0ST2nMFfhMwVymVC6wGpiml/nH1TlrrF7XWaVrrtOjo6HacToiO88HeQhb470BHJEBv6UAvPFObE7jW+t+11vFa6yRgMfC51vobLotMCJNcrLORnXOEcXovKnkBKGV2SEI0SeaBC3GVTYeKmeb4Eit2GT4RHs0lCVxrvUlrPccVxxLCbO/vLWS+73Z0zFDoMdTscIRollyBC9FIdZ2dwzn7GMFhlFx9Cw8nCVyIRjYfLmKm/QvjRfJ8c4MR4jokgQvRyPt7z3CP73Z073EQmWh2OEK0yDsSeOkx2LfO7ChEJ1dTb+f0wV0M4JQMnwiv4B0JfNsf4J/fherzZkciOrHNh4uZ5diKVlYYdo/Z4QhxXd6RwMc8DLZqyHrD7EhEJ/Zhdj53+2xH950Kwd3NDkeI6/KOBB6bAvFjYdfL4JCyK8L1aurtlOZ8QS+KsaQsMjscIW6IVyTweruDM4PuhbJjcGKz2eGITmjrkRJm2rdit/rD4DvMDkeIG+IVCfwn67KZtykGHdTNuAoXwsU+yj7NHJ8dqEGzwT/U7HCEuCFekcAXpMZTUKU52PMuOPQBXMg3OyTRidTa7FQd/IwoKrCkyOwT4T28IoFP6NeNYb3C+OXZ8WitIWOF2SGJTuSLIyXMdGyh3jcM+s8wOxwhbphXJHClFMsm92VbaQglsVMh4zWw1ZkdlugkPt5zgtus6ViG3Q0+/maHI8QN84oEDnDH8FjiIgJ5qWYaVBVBzntmhyQ6gVqbHXvOhwRTg3WEDJ8I7+I1CdzHauGBm/vwUmEfakN6w65XzA5JdAJfHi1lln0rtYEx0rhYeB2vSeAAi8f0JjTAj/cDboeT2+DsAbNDEl7u86zD3GLNwpqyACxWs8MRolW8KoEH+/vwjfGJ/DI/FW31h3S5ChdtV2dzYM15F1/s+MjsE+GFvCqBA3xzYhIVlgh2h02DPauhptzskISX+vJYCbPsW6kKSYJeo8wOR4hW87oEHhMWwN2jevHrkpugrhKy15gdkvBSX2TuZbzlIP6jFknfS+GVvC6BAzw8qS+76vtwNmSIcTNTa7NDEl6m3u4g4PA7WJTGZ4TUPhHeySsT+IAeoUwb3IMXqqZC8UE4+aXZIQkv8+WxUmbat3Ihchh0H2B2OEK0iVcmcIBlk/uyqnostT6hUh9FtNqu9K8YYTlO0OjFZociRJt5bQIf1yeKQfExvKtuQR98FyrOmh2S8BL1dgfBR97BgcI3ZYHZ4QjRZl6bwJVSPDy5L89XTkE5bJD5mtkhCS+xwzn75Fz0WAjrZXY4QrSZ1yZwgNuG9cQe2ZfdfqmQvgLsNrNDEl4ga+dm+lkKCRuzxOxQhGgXr07gPlYLD93clz9XToWKAjj8odkhCQ9nszuIOPYONnzwHX632eEI0S5encABFqbFk+k/ljJrjNzMFNf11bFiZjq+oDR2MgRGmh2OEO3i9Qk8yM+Hr0/oy99qp8LxTVByxOyQhAc7+NVH9FTniBz3dbNDEaLdvD6BA9w/IYl1TMOmfKRKoWiW3aGJOv4utSoAv6HS91J4vzYncKVUb6XURqXUQaXUfqXU464MrDWiQ/2ZmjqMD+1jcWSthLoqs0IRHmzn0QKmObZT0nsW+AWZHY4Q7daeK3Ab8EOt9RBgPPBdpdRQ14TVeg9N6svfbTOw1JbD3rVmhSE82LHt7xKhqug24V6zQxHCJdqcwLXWhVrrTOfzCuAgEOeqwFqrX3QIEYMmc5gEHDtfkvoo4goXquuJyX2XCms4AQOnmx2OEC7hkjFwpVQSMAr4yhXHa6tlU/rxWv0MLGf3Ql66maEID1JTb+c3L/2DKY5dVA+4E6y+ZockhEu0O4ErpUKAdcD3tdbXFOdWSi1TSqUrpdKLi4vbe7oWpSVGcjz2DqoIxLHrJbeeS3gHu0Pz7Gtr+Gnpf2ALiSXmjv80OyQhXKZdCVwp5YuRvFdqrdc3tY/W+kWtdZrWOi06Oro9p7uReFg6dRhv2Sah962HqhK3nk94Nq01f1r9Dt89/W+owHCCH/4AQnuaHZYQLtOeWSgKeAU4qLV+znUhtc/MoT3ZGHonVkc9OvN1s8MRJvr7ex9z76HvYfULJHTZBxDR2+yQhHCp9lyB3wTcB0xTSmU5H7NdFFebWS2KGVOmsN0+lLodL4GtzuyQhAne+Xwrt2Usw8/Hx0jeUX3NDkkIl2vPLJQvtNZKa52itR7pfHzgyuDaakFqPGt85+JflQ+v3w2V7h17F55l8850xmxeSpBVE/jQBlT0QLNDEsItOsVKzKsF+llJnDCfx+oexXY6Hf3iFCjIMjss0QF279tP3/eXEGapxedb7+AbO8zskIRwm06ZwAGWT+mLSlnAXdU/p6SyDsffbpUFPp3ckWNHiFw7n0hVif3e9QT2lk7zonPrtAk8yM+H/108imVfu4cF9l+RWd8H1j2I/vg/wWE3OzzhYgX5p/B5/W5iOMfFhasJ7z/O7JCEcLtOm8AvuWtkHCu/P4f/6flbXrfNQH35R+pfXwjV58wOTbjI+ZKzVL9yJz0ponju34kZNsXskIToEJ0+gQPERwbxj+WTqJjxG560PQQnNlP95ylQlGN2aKKdLpaXUfLCbOLt+eTOeInE1FvNDkmIDtMlEjgY0wsfmdqfxd/+OT8IfIbK8vPUvnAL9Qc2mB2aaKP6ixcoeP4OEupPsO+m/2PIzdJhR3QtXSaBXzI8PpzfPPEwfxu6ghxbT3zfvJfS938BDofZoTXPXg+H/wVrH4S/3Azbn4faSrOjMpWuq+LUn+4kqSaHHam/Y/Qs6W8puh6lO7BqX1pamk5P95wiU59nn6T67Ue5Q28hN2YGiQ++ivIPNTssg9ZGQa69b8K+dXCx1GgBFpkEBbshIALGLoNxyyG4u9nRdgy7Dc6fxF58mNMfPEvvCxn8a/AzzF7yqNmRCeFWSqkMrXXaNdu7cgIHKC6v4dMVP2dR2YsU+CUReP9quvcebF5Apccg+03IXgPnToBPAAy6HVK+Bv2mg48fnN4JX/wBDr0PPoEw6hsw8VEjuXcGVaVQesRoj1d6hLqzh6kvOkRAxSms2gZAvbbyz4SfsuCBf8Oo6iBE5yUJvAVaaz7bsJq09B+CUuwPm8LFsL7Yowbg02MQYT37ERsZTEyYP/4+1lYd2+HQlF2so7iilqKKWorKayiurKWovJaaeju+VgsR+jwjzn/GsNJ/EVu5H42iMGosp+LncLbXLCyBYfhaLfj7WAgN8GFgz1DCAnyh+BBs+6OR7LUDkufBTY9Dz+Fu+qRcpO4iVBQ6H2fg/CkoPQolR9ClR1CNZgjV4UOuowfHdS9OEEtNWF+Cew0hYeAIZo4ejNUiyVt0fpLAb0Dukb1UrH+C+JrDROoLDdtrtQ+5uifHdC8KfHpTFphEdVgfHN0GEBkZRc+wAOodmuLyGooqai8n64oaSirrsDs0oPGnHn/qCKSOKH8HaT7HudW+mfF6Dz7KwX5HIm/bb+Y9+wTOEtVirAlRQQzrFcawXmGMirjIyPxVBO19HVVXCf1nwM1PQOJN0JFXp/Z6qDxrJOXyAuNrQ6J2JuvyQqi9cM1bK327cUrFsa82mkO2nhzXvSgLSKBHwiBGJnUjNSGSlPhwgvx8Ou7nEcJDSAJvJV1VSlVBDpX5B6g/ewhL2RGCyo8TVp2PlcsLgQp1FMccsVQTQKCqJdRST7DVRpCqI1DV4a9r8dW1+NhrUDTxWYf3huELIWURju6DqbM7qLM7qLcZX+tsxqPW+fr8xToOFlZwoKCc/QUXyC292HCoPsF1PBKyidlV7xBsO0dNj1H4TfkBlsFzwNKO+9WNE/OlpFx59nJSrnA+v1gKV/2MDuVDTUB3yn2jKVVRnNERnK6P4FhNKMdrQzmjozirI6m2BDMkNpTUhEhGJUSQmhBJQlSQDI8IgSRw17HVQtkJ5xjtYexFh7EXHUI56vDxD0L5BoFvoPMRZIxhX3ruG+D8GmiMXUcmQvzYdiXXipp6Z0K/wP6CcvYXlHOqqJS72cQy6wYSLMUUWmIp9e3RquMqrQlxlBPhKCPcce0VswML5y2RnLdGcc7ajfMW4+sZRzhHqkM5Wh3KGR1JGaFo52Sn8EBfYsMDjEdEILFhxteEqCCS48Lk6lqIZkgC70LqbA6OFFVwIK8MdeAdBhS+i5+jptXHqbKEUGbpxjlrFOcszoe1G+csUZRbwnGoa+8HBPv7EBseeDlRhwcSG2E8lwQtRNtIAhdCCC/VXALvcgt5hBCis5AELoQQXkoSuBBCeClJ4EII4aUkgQshhJeSBC6EEF5KErgQQngpSeBCCOGlOnQhj1KqGDjZxrd3B0pcGI63ks/hMvksDPI5GDrz55CotY6+emOHJvD2UEqlN7USqauRz+Ey+SwM8jkYuuLnIEMoQgjhpSSBCyGEl/KmBP6i2QF4CPkcLpPPwiCfg6HLfQ5eMwYuhBDiSt50BS6EEKIRSeBCCOGlvCKBK6VuU0odUkodVUr91Ox4zKKUylVK7VVKZSmlukxnDKXU35RSRUqpfY22RSmlPlFKHXF+jTQzxo7QzOfwtFIq3/k7kaWUmm1mjB1BKdVbKbVRKXVQKbVfKfW4c3uX+53w+ASulLICzwO3A0OBJUqpoeZGZapbtNYju9h811eB267a9lPgM631AOAz5+vO7lWu/RwAfu/8nRiptf6gg2Mygw34odZ6CDAe+K4zJ3S53wmPT+DAWOCo1vq41roOWA3cZXJMogNprbcAZVdtvgt4zfn8NeDuDg3KBM18Dl2O1rpQa53pfF4BHATi6IK/E96QwOOA041e5zm3dUUa+FgplaGUWmZ2MCbrobUuBOMfNBBjcjxmelQple0cYun0wwaNKaWSgFHAV3TB3wlvSOCqiW1dde7jTVrrVIzhpO8qpSabHZAw3V+AfsBIoBD4H3PD6ThKqRBgHfB9rXW52fGYwRsSeB7Qu9HreKDApFhMpbUucH4tAt7GGF7qqs4qpWIBnF+LTI7HFFrrs1pru9baAbxEF/mdUEr5YiTvlVrr9c7NXe53whsS+C5ggFKqj1LKD1gMvGtyTB1OKRWslAq99ByYBexr+V2d2rvAUufzpcA7JsZimksJy+keusDvhFJKAa8AB7XWzzX6Vpf7nfCKlZjOqVF/AKzA37TWvzQ5pA6nlOqLcdUN4AO80VU+B6XUKmAqRrnQs8BTwD+BN4EE4BSwUGvdqW/wNfM5TMUYPtFALrD80jhwZ6WUuhnYCuwFHM7N/4ExDt61fie8IYELIYS4ljcMoQghhGiCJHAhhPBSksCFEMJLSQIXQggvJQlcCCG8lCRwIYTwUpLARZejlEpSSn29De97VSm1oA3v+6ZSqldr3yfE9UgCF15NKeXThrclAa1O4O3wTUASuHA5SeDC4yml7ndW29ujlHrdeSX8nFJqI/AbZ5mBvymldimldiul7nK+L0kptVUplel8THQe8r+BSc4GCE8opaxKqd8535+tlFrufL9SSv1JKXVAKfU+16lup5T6ufMY+5RSLzrfvwBIA1Y6zxfovk9KdDlaa3nIw2MfwDDgENDd+ToKo7HBBsDq3PYr4BvO5xHAYSAYCAICnNsHAOnO51OBDY3OsQz4mfO5P5AO9AHmAZ9glHDoBZwHFrQQa1Sj568DdzqfbwLSzP4s5dH5Hm3581OIjjQNWKu1LgHQWpcZtYx4S2ttd+4zC5irlPqR83UARj2MAuBPSqmRgB0Y2Mw5ZgEpjca3wzES/mRglfM8BUqpz68T6y1KqR9j/McRBewH3mvVTytEK0gCF55O0XT996qr9pmvtT50xRuVehqj6NMIjOHCmhbO8T2t9b+uev/sZs597QGUCgD+jHGlfdp57oAbea8QbSVj4MLTfQYsUkp1A6NxbRP7/Av4nrPMKEqpUc7t4UChNmpl34cxFAJQAYRe9f7vOGtMo5Qa6CzZuwVY7BwjjwVuaSHOS8m6xNlooPFslavPJ4RLyBW48Gha6/1KqV8Cm5VSdmB3E7v9AqPccLYziecCczCuiNcppRYCG7l81Z4N2JRSezDG0/8XY2ZKpvP9xRj9FN/GGMLZizGuvrmFOM8rpV5y7puLUcf+kleBF5RS1cAErXV1qz4EIZoh5WSFEMJLyRCKEEJ4KRlCEaKVlFJvY0wzbOwnV98EFcLdZAhFCCG8lAyhCCGEl5IELoQQXkoSuBBCeClJ4EII4aX+P2ITXFvyiK/1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack(level=0)\n",
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack(level=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
